From a3254924a8b06d2983381d2279ba822827918c8a Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 17:39:30 +0100
Subject: [PATCH 01/24] perf(kernel): skip the discarded SoC series, and add a
 batch entry point

Two changes to the kernel, one that pays off immediately and one that lays the
groundwork for running a fan-out of scenarios in parallel.

1. Do not compute the per-minute SoC series when nobody reads it (-13%)

run_prediction_kernel allocated an n_steps buffer for every scenario and told the
kernel to fill it, but a cached run - which is 19,209 of the 25,727 predictions in
a plan - discards it immediately (see the `if not cache` block). Filling it cost a
round_py per step, and round_py is snprintf+strtod at ~96ns, by far the most
expensive thing in the hot loop. soc is never itself rounded, the result only ever
fed soc_out, so skipping it cannot change the simulation.

  52.8us -> 28.5us per scenario (1.85x), and the 20 scenario benchmark
  42.5s -> 36.9s (-13.1%), byte-identical on all 20 and kernel_parity green.

2. pk_run_batch, so a fan-out can be run in one call

Adds PkBatchJob/PkBatchResult and pk_run_batch(handle, jobs, n, results,
n_threads), sharing pk_run_one with pk_run so there is a single copy of the
simulation. Additive: pk_run keeps its signature and PkScenario keeps its layout,
and Python probes for the batch symbol (KERNEL_HAS_BATCH), so a binary built
before this still loads and runs every scenario the old way.

Batched jobs pass soc_out = null. The one thing a caller still wants from the
series is the SoC range across a charge window, so the kernel tracks that inline
over an optional step range instead, mirroring the predict_soc scan in
thread_run_prediction_charge_min_max - materialising a buffer per job would have
cost ~84MB on a large batch.

Verified bit-identical against a loop of pk_run over 120 scenarios, every field.

Threading is present but NOT yet usable, and n_threads should stay 1: the
remaining round_py(soc, 1) at the charge-rate curve lookup is load bearing, and
snprintf/strtod serialise on the C library's global locale. With constant work per
thread it degrades 17x at 8 threads, which is why 4 threads currently measures
0.6x of serial. A per-thread locale (newlocale + uselocale) takes that from 17.3x
to 2.2x in isolation and is the next step before the batch path is wired up in
Python.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/prediction_kernel.cpp | 220 +++++++++++++++++++++++++++--
 apps/predbat/prediction_kernel.py  |  50 ++++++-
 2 files changed, 254 insertions(+), 16 deletions(-)

diff --git a/apps/predbat/prediction_kernel.cpp b/apps/predbat/prediction_kernel.cpp
index 1746a6497..3f00d46e2 100644
--- a/apps/predbat/prediction_kernel.cpp
+++ b/apps/predbat/prediction_kernel.cpp
@@ -24,6 +24,7 @@
 #include <map>
 #include <memory>
 #include <mutex>
+#include <thread>
 #include <vector>
 
 #define PK_ABI_VERSION 3
@@ -184,6 +185,60 @@ struct PkResult {
     int32_t iboost_running_full;
 };
 
+// One scenario in a pk_run_batch() call; field order MUST match the ctypes Structure in
+// prediction_kernel.py.
+//
+// This is deliberately a separate type from PkScenario rather than extra fields on it: pk_run keeps
+// its existing layout, so a kernel binary predating the batch entry point still loads and runs, and
+// Python falls back to looping pk_run when pk_run_batch is absent.
+//
+// soc_out may be null. A batched scenario is always a cached (non-saving) run, whose caller discards
+// the per-minute SoC series, and materialising one buffer per job would cost ~84MB on a large batch.
+// The one thing callers do want from the series is the SoC range across a charge window, so the
+// kernel tracks that inline over [soc_range_start_step, soc_range_end_step] instead - set
+// soc_range_start_step < 0 to skip it.
+struct PkBatchJob {
+    const double *charge_limit;
+    const int32_t *charge_start;
+    const int32_t *charge_end;
+    const double *export_limits;
+    const int32_t *export_start;
+    const int32_t *export_end;
+    double *soc_out; // optional, null to skip
+
+    int32_t n_charge;
+    int32_t n_export;
+    int32_t pv_scenario;
+    int32_t end_record;
+    int32_t step;
+    int32_t soc_range_start_step; // <0 to skip the SoC range scan
+    int32_t soc_range_end_step;   // inclusive
+};
+
+// Result for one batched scenario; field order MUST match the ctypes Structure in
+// prediction_kernel.py. soc_range_min/max mirror thread_run_prediction_charge_min_max's scan over
+// predict_soc and are only meaningful when the job asked for a range.
+struct PkBatchResult {
+    PkResult result;
+    double soc_range_min;
+    double soc_range_max;
+    int32_t status; // 0 = ok, matching pk_run's return codes otherwise
+    int32_t pad;
+};
+
+// Reusable per-simulation scratch buffers.
+//
+// build_window_membership assigns n_steps entries and the clip helpers clear-then-push, so all of
+// these can be reused across scenarios without reallocating - which matters a lot under threading:
+// each scenario would otherwise heap-allocate ~70KB, and 1200 of those per batch put every worker
+// thread in contention on the allocator. Measured 4 threads at 0.6x of serial before this, because
+// the simulation is cheap enough that malloc, not arithmetic, was the bottleneck.
+struct PkScratch {
+    std::vector<int32_t> charge_member, export_member;
+    std::vector<int32_t> clipped_start, clipped_end;
+    std::vector<double> clipped_limit;
+};
+
 // Deep-copied context storage so Python-side buffers can be freed after create
 struct ContextStore {
     std::vector<double> rate_import, rate_export, alert_keep;
@@ -422,20 +477,37 @@ void pk_context_free(int64_t handle)
 // Run one prediction scenario. Returns 0 on success, non-zero on error.
 // Mirrors the hot loop of Prediction.run_prediction() (prediction.py:385-1200) for the
 // supported configuration: save=None, debug off, step=5 (cars, iBoost and carbon included).
-int32_t pk_run(int64_t handle, const PkScenario *s, PkResult *out)
+// Look up a context by handle, or null when the handle is unknown. The returned pointer stays valid
+// as long as the caller does not free the context concurrently, which no caller does: contexts are
+// created once per plan and released by a weakref finaliser once the Prediction is gone.
+static const PkContext *lookup_context(int64_t handle)
 {
-    const PkContext *c;
-    {
-        std::lock_guard<std::mutex> lock(g_context_mutex);
-        auto it = g_contexts.find(handle);
-        if (it == g_contexts.end()) {
-            return 1;
-        }
-        c = &it->second->ctx;
+    std::lock_guard<std::mutex> lock(g_context_mutex);
+    auto it = g_contexts.find(handle);
+    if (it == g_contexts.end()) {
+        return nullptr;
     }
+    return &it->second->ctx;
+}
+
+// Simulate one scenario against an already-resolved context.
+//
+// Shared by pk_run and pk_run_batch, and safe to call concurrently on one context: c is const, every
+// working value below is a local, and results go only to the caller's own out/soc_range slots.
+//
+// soc_out may be null (see PkBatchJob). soc_range_min/max are optional out-params: when non-null and
+// soc_range_start_step >= 0, they receive the min/max of the rounded SoC over that inclusive step
+// range, mirroring the predict_soc scan in Prediction.thread_run_prediction_charge_min_max.
+static int32_t pk_run_one(const PkContext *c, const PkScenario *s, PkResult *out, int32_t soc_range_start_step, int32_t soc_range_end_step, double *soc_range_min_out, double *soc_range_max_out, PkScratch &scratch)
+{
     if (!s || !out || s->step != 5) {
         return 2;
     }
+    // Seeded to match Prediction.thread_run_prediction_charge_min_max, which starts min at soc_max
+    // and max at 0 so a range covering no recorded step leaves them in that state
+    double soc_range_min = c->soc_max;
+    double soc_range_max = 0.0;
+    const bool want_soc_range = soc_range_start_step >= 0;
 
     const int32_t pv_scenario = s->pv_scenario;
     const bool is_pv10 = pv_scenario == 1;
@@ -445,12 +517,14 @@ int32_t pk_run(int64_t handle, const PkScenario *s, PkResult *out)
     const bool inverter_hybrid = c->inverter_hybrid != 0;
 
     // Window membership - prediction.py:494-495 / find_charge_window_optimised
-    std::vector<int32_t> charge_window_optimised, export_window_optimised;
+    std::vector<int32_t> &charge_window_optimised = scratch.charge_member;
+    std::vector<int32_t> &export_window_optimised = scratch.export_member;
 
     // The caller hands over the raw charge windows; clipping them against the export windows used to
     // be done in Python on every simulation, which cost more than the simulation itself
-    std::vector<int32_t> clipped_start, clipped_end;
-    std::vector<double> clipped_limit;
+    std::vector<int32_t> &clipped_start = scratch.clipped_start;
+    std::vector<int32_t> &clipped_end = scratch.clipped_end;
+    std::vector<double> &clipped_limit = scratch.clipped_limit;
     clip_intersecting_charge_windows(clipped_start, clipped_end, clipped_limit, s->n_charge, s->charge_start, s->charge_end, s->charge_limit, s->n_export, s->export_start, s->export_end, s->export_limits);
     const int32_t n_charge_clipped = static_cast<int32_t>(clipped_start.size());
 
@@ -570,8 +644,28 @@ int32_t pk_run(int64_t handle, const PkScenario *s, PkResult *out)
             record = false;
         }
 
-        // Save SoC prediction data - prediction.py:627-628 (kernel always fills the buffer)
-        s->soc_out[k] = round_py(soc, 3);
+        // Save SoC prediction data - prediction.py:627-628. Batched scenarios pass a null buffer
+        // (their caller discards the series); the rounded value is still what feeds the SoC range,
+        // so the range matches a Python scan over predict_soc exactly.
+        // round_py is snprintf+strtod, ~96ns and by far the most expensive thing in this loop, so it
+        // is only paid when something actually consumes the value. soc itself is never rounded - the
+        // result feeds soc_out and the SoC range and nothing else - so skipping it when a batched
+        // scenario wants neither cannot change the simulation.
+        const bool in_soc_range = want_soc_range && k >= soc_range_start_step && k <= soc_range_end_step;
+        if (s->soc_out || in_soc_range) {
+            const double soc_rounded = round_py(soc, 3);
+            if (s->soc_out) {
+                s->soc_out[k] = soc_rounded;
+            }
+            if (in_soc_range) {
+                if (soc_rounded < soc_range_min) {
+                    soc_range_min = soc_rounded;
+                }
+                if (soc_rounded > soc_range_max) {
+                    soc_range_max = soc_rounded;
+                }
+            }
+        }
 
         // Get load and pv forecast - prediction.py:657-659
         double pv_now = pv_step[k];
@@ -1074,7 +1168,105 @@ int32_t pk_run(int64_t handle, const PkScenario *s, PkResult *out)
     out->iboost_running = iboost_running;
     out->iboost_running_solar = iboost_running_solar;
     out->iboost_running_full = iboost_running_full;
+    if (soc_range_min_out) {
+        // Mirrors the two clamping lines that follow the scan in
+        // thread_run_prediction_charge_min_max, so an empty range collapses to a single value
+        // rather than leaving min above max
+        const double clamped_max = soc_range_max > soc_range_min ? soc_range_max : soc_range_min;
+        const double clamped_min = soc_range_min < clamped_max ? soc_range_min : clamped_max;
+        *soc_range_min_out = clamped_min;
+        *soc_range_max_out = clamped_max;
+    }
+    return 0;
+}
+
+// Run one scenario. Kept with its original signature and struct so a binary built before the batch
+// entry point existed still satisfies the loader and every single-scenario caller.
+int32_t pk_run(int64_t handle, const PkScenario *s, PkResult *out)
+{
+    const PkContext *c = lookup_context(handle);
+    if (!c) {
+        return 1;
+    }
+    PkScratch scratch;
+    return pk_run_one(c, s, out, -1, -1, nullptr, nullptr, scratch);
+}
+
+// Run n_jobs scenarios against one context in a single call.
+//
+// The point is to pay the Python/C boundary and the context lookup once for a whole fan-out of
+// scenarios instead of once each: the per-call Python around pk_run measured ~31us against ~55us of
+// simulation, and neither Python threads nor a process pool can recover it (the GIL serialises the
+// former, pickling the latter).
+//
+// Per-job failures are reported in that job's status field rather than aborting the batch, so one
+// malformed scenario cannot discard the rest of the fan-out.
+// Simulate one batched scenario into its result slot. Split out so the serial and threaded paths
+// below share exactly one copy of the marshalling.
+static void run_batch_job(const PkContext *c, const PkBatchJob &job, PkBatchResult &out, PkScratch &scratch)
+{
+    PkScenario scenario;
+    scenario.charge_limit = job.charge_limit;
+    scenario.charge_start = job.charge_start;
+    scenario.charge_end = job.charge_end;
+    scenario.export_limits = job.export_limits;
+    scenario.export_start = job.export_start;
+    scenario.export_end = job.export_end;
+    scenario.soc_out = job.soc_out;
+    scenario.n_charge = job.n_charge;
+    scenario.n_export = job.n_export;
+    scenario.pv_scenario = job.pv_scenario;
+    scenario.end_record = job.end_record;
+    scenario.step = job.step;
+    out.soc_range_min = 0.0;
+    out.soc_range_max = 0.0;
+    out.pad = 0;
+    out.status = pk_run_one(c, &scenario, &out.result, job.soc_range_start_step, job.soc_range_end_step, &out.soc_range_min, &out.soc_range_max, scratch);
+}
+
+// Run n_jobs scenarios against one context in a single call.
+//
+// The context lookup and the Python/C boundary are paid once for a whole fan-out rather than once
+// per scenario, but the real point is n_threads: 19209 separate ctypes calls cannot be parallelised
+// usefully from Python (the GIL serialises threads, and a process pool pays pickling), whereas one
+// batch can split the work across cores with no GIL involved at all.
+//
+// n_threads <= 1 runs serially. Per-job failures land in that job's status field rather than
+// aborting the batch, so one malformed scenario cannot discard the rest of the fan-out.
+int32_t pk_run_batch(int64_t handle, const PkBatchJob *jobs, int32_t n_jobs, PkBatchResult *results, int32_t n_threads)
+{
+    const PkContext *c = lookup_context(handle);
+    if (!c) {
+        return 1;
+    }
+    if (!jobs || !results || n_jobs < 0) {
+        return 2;
+    }
+    // Scenarios are independent - each reads the shared const context and writes only its own result
+    // slot - so they are split across threads by stride.
+    if (n_threads > 1 && n_jobs > 1) {
+        const int32_t use = n_threads < n_jobs ? n_threads : n_jobs;
+        std::vector<std::thread> pool;
+        pool.reserve(use);
+        for (int32_t t = 0; t < use; t++) {
+            pool.emplace_back([c, jobs, results, n_jobs, use, t]() {
+                PkScratch scratch;
+                for (int32_t i = t; i < n_jobs; i += use) {
+                    run_batch_job(c, jobs[i], results[i], scratch);
+                }
+            });
+        }
+        for (auto &th : pool) {
+            th.join();
+        }
+        return 0;
+    }
+    PkScratch scratch;
+    for (int32_t i = 0; i < n_jobs; i++) {
+        run_batch_job(c, jobs[i], results[i], scratch);
+    }
     return 0;
 }
 
+
 } // extern "C"
diff --git a/apps/predbat/prediction_kernel.py b/apps/predbat/prediction_kernel.py
index 2275e9951..7d90317cd 100644
--- a/apps/predbat/prediction_kernel.py
+++ b/apps/predbat/prediction_kernel.py
@@ -41,6 +41,9 @@
 KERNEL_LIB = None
 KERNEL_LOAD_TRIED = False
 KERNEL_STATUS = "not loaded"
+# True when the loaded binary exposes pk_run_batch. Probed rather than required so a kernel built
+# before the batch entry point existed still runs every scenario through the single-call path.
+KERNEL_HAS_BATCH = False
 
 
 class PkContext(ctypes.Structure):
@@ -176,6 +179,39 @@ class PkResult(ctypes.Structure):
     ]
 
 
+class PkBatchJob(ctypes.Structure):
+    """ctypes mirror of the PkBatchJob struct in prediction_kernel.cpp (field order must match exactly)"""
+
+    _fields_ = [
+        ("charge_limit", ctypes.POINTER(ctypes.c_double)),
+        ("charge_start", ctypes.POINTER(ctypes.c_int32)),
+        ("charge_end", ctypes.POINTER(ctypes.c_int32)),
+        ("export_limits", ctypes.POINTER(ctypes.c_double)),
+        ("export_start", ctypes.POINTER(ctypes.c_int32)),
+        ("export_end", ctypes.POINTER(ctypes.c_int32)),
+        ("soc_out", ctypes.POINTER(ctypes.c_double)),
+        ("n_charge", ctypes.c_int32),
+        ("n_export", ctypes.c_int32),
+        ("pv_scenario", ctypes.c_int32),
+        ("end_record", ctypes.c_int32),
+        ("step", ctypes.c_int32),
+        ("soc_range_start_step", ctypes.c_int32),
+        ("soc_range_end_step", ctypes.c_int32),
+    ]
+
+
+class PkBatchResult(ctypes.Structure):
+    """ctypes mirror of the PkBatchResult struct in prediction_kernel.cpp (field order must match exactly)"""
+
+    _fields_ = [
+        ("result", PkResult),
+        ("soc_range_min", ctypes.c_double),
+        ("soc_range_max", ctypes.c_double),
+        ("status", ctypes.c_int32),
+        ("pad", ctypes.c_int32),
+    ]
+
+
 def kernel_library_candidates():
     """Return the ordered list of shared library paths to try loading.
 
@@ -220,7 +256,7 @@ def load_kernel(log=None):
     The result is cached module-wide (including across forked workers); failures
     are logged once and result in a permanent fallback to the Python engine.
     """
-    global KERNEL_LIB, KERNEL_LOAD_TRIED, KERNEL_STATUS
+    global KERNEL_LIB, KERNEL_LOAD_TRIED, KERNEL_STATUS, KERNEL_HAS_BATCH
 
     if KERNEL_LOAD_TRIED:
         return KERNEL_LIB
@@ -248,6 +284,12 @@ def load_kernel(log=None):
             lib.pk_context_free.argtypes = [ctypes.c_int64]
             lib.pk_run.restype = ctypes.c_int32
             lib.pk_run.argtypes = [ctypes.c_int64, ctypes.POINTER(PkScenario), ctypes.POINTER(PkResult)]
+            try:
+                lib.pk_run_batch.restype = ctypes.c_int32
+                lib.pk_run_batch.argtypes = [ctypes.c_int64, ctypes.POINTER(PkBatchJob), ctypes.c_int32, ctypes.POINTER(PkBatchResult)]
+                KERNEL_HAS_BATCH = True
+            except AttributeError:
+                KERNEL_HAS_BATCH = False
             KERNEL_LIB = lib
             KERNEL_STATUS = "loaded from {}".format(path)
             if log:
@@ -523,7 +565,11 @@ def run_prediction_kernel(pred, charge_limit, charge_window, export_window, expo
     scenario.export_limits = double_array(export_limits)
     scenario.export_start = int32_array([window["start"] for window in export_window])
     scenario.export_end = int32_array([window["end"] for window in export_window])
-    soc_out = (ctypes.c_double * n_steps)()
+    # A cached run discards the per-minute SoC series (see the `if not cache` block below), so the
+    # buffer is not allocated and the kernel is told to skip filling it. That skips a round_py per
+    # step, which is snprintf+strtod and the single most expensive thing in the kernel's hot loop -
+    # measured 52.8us -> 28.5us per scenario, for a value that was being thrown away.
+    soc_out = (ctypes.c_double * n_steps)() if not cache else None
     scenario.soc_out = soc_out
     scenario.n_charge = len(charge_window)
     scenario.n_export = len(export_window)

From 2e71022e7f610155e787b045adfa9d8772c48ca6 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 17:50:39 +0100
Subject: [PATCH 02/24] perf(kernel): replace the hot-loop round_py with
 precomputed SoC percent buckets

round_py is snprintf+strtod, ~96ns, and was the most expensive thing in the
simulation loop. It survives in the loop for one reason: prediction.py rounds SoC
to 1dp before the charge/discharge rate curve lookup.

That rounding is not a modelling decision. It exists so utils.get_charge_rate_curve_cached's
lru_cache key quantises and the cache hits - a Python memoisation device. The
kernel has no such cache and gains nothing from it, but rounding before the
percent is taken occasionally moves the resulting integer, so it is observable and
parity forced the kernel to reproduce it.

Everything downstream only wants an integer 0-100: round(soc,1) -> calc_percent_limit
-> curve[percent]. That map is monotone onto 101 buckets, so the boundaries can be
found once and the loop reduced to a binary search over them. The boundaries are
located by bisecting the double's bit pattern (non-negative doubles order the same
as their bits read as integers), so each one is the true boundary to the last bit
rather than an approximation.

This is exact, not approximate: pk_verify_soc_percent_table sweeps 2,000,000 SoC
values plus five probes around all 101 boundaries - where a disagreement would
actually hide - for ten battery sizes from 2 to 100 kWh. Zero disagreements.

Per scenario, on a 1200 scenario batch:

  52.8us  original
  28.5us  after skipping the discarded SoC series
  10.9us  now                                        4.8x on serial

and it unblocks parallelism, because the contention was libc's global locale
inside snprintf/strtod, not the simulation:

   1 thread   13.0 ms
   2 threads   6.7 ms  1.9x
   4 threads   3.5 ms  3.8x
   8 threads   2.4 ms  5.4x
  16 threads   1.9 ms  6.7x     bit-identical at every thread count

The 20 scenario benchmark is 36.9s -> 31.6s even before the Python side batches
anything, byte-identical on all 20, and kernel_parity passes. No ABI change: the
bucket table lives on ContextStore, which never crosses the Python boundary, so
PkContext keeps its layout.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/prediction_kernel.cpp | 147 ++++++++++++++++++++++++++---
 1 file changed, 134 insertions(+), 13 deletions(-)

diff --git a/apps/predbat/prediction_kernel.cpp b/apps/predbat/prediction_kernel.cpp
index 3f00d46e2..18d8840a5 100644
--- a/apps/predbat/prediction_kernel.cpp
+++ b/apps/predbat/prediction_kernel.cpp
@@ -17,6 +17,7 @@
 
 #include <algorithm>
 #include <cstdint>
+#include <limits>
 #include <cstdio>
 #include <cstdlib>
 #include <cstring>
@@ -56,6 +57,71 @@ int32_t calc_percent_limit(double charge_limit, double soc_max)
     return std::min(static_cast<int32_t>((charge_limit / soc_max * 100.0) + 0.5), 100);
 }
 
+// The SoC percent that Python's round(soc, 1) -> calc_percent_limit() pair produces.
+//
+// The 1dp rounding is not a modelling decision - it is there so utils.get_charge_rate_curve_cached's
+// lru_cache hits - but rounding before the percent is taken occasionally moves the integer, so the
+// kernel has to reproduce it exactly.
+int32_t percent_via_round(double soc, double soc_max)
+{
+    return calc_percent_limit(round_py(soc, 1), soc_max);
+}
+
+// Smallest SoC whose percent_via_round() reaches p, found by bisecting the double's bit pattern.
+//
+// Non-negative doubles compare in the same order as their bit patterns read as integers, so this is
+// an exact bisection over every representable value in the bracket rather than an approximation:
+// the boundary it returns is the true one, to the last bit.
+double smallest_soc_for_percent(double soc_max, int32_t p, double hi_d)
+{
+    if (percent_via_round(0.0, soc_max) >= p) {
+        return 0.0;
+    }
+    if (percent_via_round(hi_d, soc_max) < p) {
+        return std::numeric_limits<double>::infinity();
+    }
+    uint64_t lo_bits, hi_bits;
+    double lo_d = 0.0;
+    std::memcpy(&lo_bits, &lo_d, sizeof(lo_bits));
+    std::memcpy(&hi_bits, &hi_d, sizeof(hi_bits));
+    while (hi_bits - lo_bits > 1) {
+        const uint64_t mid_bits = lo_bits + (hi_bits - lo_bits) / 2;
+        double mid_d;
+        std::memcpy(&mid_d, &mid_bits, sizeof(mid_d));
+        if (percent_via_round(mid_d, soc_max) >= p) {
+            hi_bits = mid_bits;
+        } else {
+            lo_bits = mid_bits;
+        }
+    }
+    double result;
+    std::memcpy(&result, &hi_bits, sizeof(result));
+    return result;
+}
+
+// Boundaries of the 101 SoC percent buckets, so the hot loop can map SoC to percent with a binary
+// search over doubles instead of a round_py (snprintf + strtod, ~96ns and the most expensive thing
+// in the loop - and unusable under threading, since it serialises on the C library's global locale).
+// out[p] is the smallest SoC that reads as p percent; out[0] is unused.
+void build_soc_percent_thresholds(std::vector<double> &out, double soc_max)
+{
+    out.assign(101, 0.0);
+    if (soc_max <= 0) {
+        return;
+    }
+    const double hi = soc_max * 2.0 + 1.0;
+    for (int32_t p = 1; p <= 100; p++) {
+        out[p] = smallest_soc_for_percent(soc_max, p, hi);
+    }
+    // upper_bound needs a non-decreasing sequence; the map is monotone so this only guards against
+    // an unreachable percent leaving an infinity in front of a later finite entry
+    for (int32_t p = 2; p <= 100; p++) {
+        if (out[p] < out[p - 1]) {
+            out[p] = out[p - 1];
+        }
+    }
+}
+
 // Per-plan static context passed from Python, arrays are all n_steps long
 // (index k covers relative minute k*5) unless stated otherwise.
 // Field order MUST match the ctypes Structure in prediction_kernel.py exactly.
@@ -248,6 +314,7 @@ struct ContextStore {
     std::vector<double> charge_curve, discharge_curve;
     std::vector<double> carbon, gas_rate, iboost_plan_load;
     std::vector<double> car_load_flat, car_rate_flat;
+    std::vector<double> soc_percent_threshold; // see build_soc_percent_thresholds
     PkContext ctx;
 };
 
@@ -277,6 +344,14 @@ inline double get_total_inverted(double battery_draw, double pv_dc, double pv_ac
 // Mirror of utils.py get_charge_rate_curve_cached()/get_discharge_rate_curve_cached().
 // soc_key is passed exactly as Python does (usually round(soc, 1), but raw soc for the DC-rate lookups).
 // temp_cap_base is the pre-computed find_battery_temperature_cap() value before the min with max_rate.
+inline double rate_curve_pct(int32_t soc_percent, double rate_setting, double rate_max, double temp_cap_base, const double *curve, double rate_min)
+{
+    double max_rate = rate_max * curve[soc_percent];
+    double max_rate_cap = std::min(temp_cap_base, rate_max);
+    max_rate = std::min(max_rate, max_rate_cap);
+    return std::max(std::min(rate_setting, max_rate), rate_min);
+}
+
 inline double rate_curve(double soc_key, double rate_setting, double rate_max, double temp_cap_base, const double *curve, double soc_max, double rate_min)
 {
     int32_t soc_percent = calc_percent_limit(soc_key, soc_max);
@@ -417,6 +492,7 @@ int64_t pk_context_create(const PkContext *in)
         return 0;
     }
     auto store = std::make_unique<ContextStore>();
+    build_soc_percent_thresholds(store->soc_percent_threshold, in->soc_max);
     size_t n = static_cast<size_t>(in->n_steps);
     store->rate_import.assign(in->rate_import, in->rate_import + n);
     store->rate_export.assign(in->rate_export, in->rate_export + n);
@@ -480,14 +556,14 @@ void pk_context_free(int64_t handle)
 // Look up a context by handle, or null when the handle is unknown. The returned pointer stays valid
 // as long as the caller does not free the context concurrently, which no caller does: contexts are
 // created once per plan and released by a weakref finaliser once the Prediction is gone.
-static const PkContext *lookup_context(int64_t handle)
+static const ContextStore *lookup_context(int64_t handle)
 {
     std::lock_guard<std::mutex> lock(g_context_mutex);
     auto it = g_contexts.find(handle);
     if (it == g_contexts.end()) {
         return nullptr;
     }
-    return &it->second->ctx;
+    return it->second.get();
 }
 
 // Simulate one scenario against an already-resolved context.
@@ -498,8 +574,10 @@ static const PkContext *lookup_context(int64_t handle)
 // soc_out may be null (see PkBatchJob). soc_range_min/max are optional out-params: when non-null and
 // soc_range_start_step >= 0, they receive the min/max of the rounded SoC over that inclusive step
 // range, mirroring the predict_soc scan in Prediction.thread_run_prediction_charge_min_max.
-static int32_t pk_run_one(const PkContext *c, const PkScenario *s, PkResult *out, int32_t soc_range_start_step, int32_t soc_range_end_step, double *soc_range_min_out, double *soc_range_max_out, PkScratch &scratch)
+static int32_t pk_run_one(const ContextStore *store, const PkScenario *s, PkResult *out, int32_t soc_range_start_step, int32_t soc_range_end_step, double *soc_range_min_out, double *soc_range_max_out, PkScratch &scratch)
 {
+    const PkContext *c = &store->ctx;
+    const std::vector<double> &soc_pct_threshold = store->soc_percent_threshold;
     if (!s || !out || s->step != 5) {
         return 2;
     }
@@ -794,11 +872,14 @@ static int32_t pk_run_one(const PkContext *c, const PkScenario *s, PkResult *out
             }
         }
 
-        // Current real charge rate - prediction.py:777-786
-        const double soc_round1 = round_py(soc, 1);
-        double charge_rate_now_curve = rate_curve(soc_round1, charge_rate_now, battery_rate_max_charge, c->temp_charge_cap[k], c->charge_curve, soc_max, battery_rate_min) * battery_rate_max_scaling;
+        // Current real charge rate - prediction.py:777-786.
+        // Python rounds SoC to 1dp here purely to quantise an lru_cache key, but the rounding moves
+        // the percent at bucket edges so it is observable. The precomputed bucket boundaries give
+        // the identical percent from a binary search, with no round_py in the loop at all.
+        const int32_t soc_percent_round1 = soc >= 0.0 ? static_cast<int32_t>(std::upper_bound(soc_pct_threshold.begin() + 1, soc_pct_threshold.end(), soc) - (soc_pct_threshold.begin() + 1)) : percent_via_round(soc, soc_max);
+        double charge_rate_now_curve = rate_curve_pct(soc_percent_round1, charge_rate_now, battery_rate_max_charge, c->temp_charge_cap[k], c->charge_curve, battery_rate_min) * battery_rate_max_scaling;
         double charge_rate_now_curve_step = charge_rate_now_curve * step;
-        double discharge_rate_now_curve = rate_curve(soc_round1, discharge_rate_now, battery_rate_max_discharge, c->temp_discharge_cap[k], c->discharge_curve, soc_max, battery_rate_min) * battery_rate_max_scaling_discharge;
+        double discharge_rate_now_curve = rate_curve_pct(soc_percent_round1, discharge_rate_now, battery_rate_max_discharge, c->temp_discharge_cap[k], c->discharge_curve, battery_rate_min) * battery_rate_max_scaling_discharge;
         double discharge_rate_now_curve_step = discharge_rate_now_curve * step;
 
         const double battery_to_min = std::max(soc - reserve_expected, 0.0) * battery_loss_discharge;
@@ -821,7 +902,7 @@ static int32_t pk_run_one(const PkContext *c, const PkScenario *s, PkResult *out
                 export_rate_adjust = 1 - (export_limit_now - static_cast<double>(static_cast<int64_t>(export_limit_now)));
             }
             discharge_rate_now = battery_rate_max_export * export_rate_adjust;
-            discharge_rate_now_curve = rate_curve(soc_round1, discharge_rate_now, battery_rate_max_export, c->temp_discharge_cap[k], c->discharge_curve, soc_max, battery_rate_min) * battery_rate_max_scaling_discharge;
+            discharge_rate_now_curve = rate_curve_pct(soc_percent_round1, discharge_rate_now, battery_rate_max_export, c->temp_discharge_cap[k], c->discharge_curve, battery_rate_min) * battery_rate_max_scaling_discharge;
             discharge_rate_now_curve_step = discharge_rate_now_curve * step;
 
             battery_draw = std::min(discharge_rate_now_curve_step, battery_to_min);
@@ -907,7 +988,7 @@ static int32_t pk_run_one(const PkContext *c, const PkScenario *s, PkResult *out
             // find_charge_rate with set_charge_low_power off (always the case for scenario runs)
             // reduces to the max rate and its curve value - utils.py:1145,1237-1238
             charge_rate_now = battery_rate_max_charge_combined;
-            charge_rate_now_curve = rate_curve(soc_round1, battery_rate_max_charge_combined, battery_rate_max_charge_combined, c->temp_charge_cap[k], c->charge_curve, soc_max, battery_rate_min) * battery_rate_max_scaling;
+            charge_rate_now_curve = rate_curve_pct(soc_percent_round1, battery_rate_max_charge_combined, battery_rate_max_charge_combined, c->temp_charge_cap[k], c->charge_curve, battery_rate_min) * battery_rate_max_scaling;
             charge_rate_now_curve_step = charge_rate_now_curve * step;
 
             battery_draw = -std::max({std::min(charge_rate_now_curve_step, std::max(charge_limit_n - soc, pv_now)), 0.0, -battery_to_max});
@@ -1184,12 +1265,12 @@ static int32_t pk_run_one(const PkContext *c, const PkScenario *s, PkResult *out
 // entry point existed still satisfies the loader and every single-scenario caller.
 int32_t pk_run(int64_t handle, const PkScenario *s, PkResult *out)
 {
-    const PkContext *c = lookup_context(handle);
+    const ContextStore *c = lookup_context(handle);
     if (!c) {
         return 1;
     }
     PkScratch scratch;
-    return pk_run_one(c, s, out, -1, -1, nullptr, nullptr, scratch);
+    return pk_run_one(c, s, out, -1, -1, nullptr, nullptr, scratch);  // c is the store
 }
 
 // Run n_jobs scenarios against one context in a single call.
@@ -1203,7 +1284,7 @@ int32_t pk_run(int64_t handle, const PkScenario *s, PkResult *out)
 // malformed scenario cannot discard the rest of the fan-out.
 // Simulate one batched scenario into its result slot. Split out so the serial and threaded paths
 // below share exactly one copy of the marshalling.
-static void run_batch_job(const PkContext *c, const PkBatchJob &job, PkBatchResult &out, PkScratch &scratch)
+static void run_batch_job(const ContextStore *c, const PkBatchJob &job, PkBatchResult &out, PkScratch &scratch)
 {
     PkScenario scenario;
     scenario.charge_limit = job.charge_limit;
@@ -1235,7 +1316,7 @@ static void run_batch_job(const PkContext *c, const PkBatchJob &job, PkBatchResu
 // aborting the batch, so one malformed scenario cannot discard the rest of the fan-out.
 int32_t pk_run_batch(int64_t handle, const PkBatchJob *jobs, int32_t n_jobs, PkBatchResult *results, int32_t n_threads)
 {
-    const PkContext *c = lookup_context(handle);
+    const ContextStore *c = lookup_context(handle);
     if (!c) {
         return 1;
     }
@@ -1269,4 +1350,44 @@ int32_t pk_run_batch(int64_t handle, const PkBatchJob *jobs, int32_t n_jobs, PkB
 }
 
 
+// Test hook: sweep SoC densely and confirm the precomputed bucket boundaries give exactly the same
+// percent as the round_py path they replace. Returns the number of disagreements (0 = equivalent).
+int32_t pk_verify_soc_percent_table(double soc_max, int32_t samples)
+{
+    std::vector<double> table;
+    build_soc_percent_thresholds(table, soc_max);
+    int32_t bad = 0;
+    const double hi = soc_max * 1.2 + 0.5;
+    for (int32_t i = 0; i <= samples; i++) {
+        const double soc = hi * static_cast<double>(i) / static_cast<double>(samples);
+        const int32_t want = percent_via_round(soc, soc_max);
+        const int32_t got = static_cast<int32_t>(std::upper_bound(table.begin() + 1, table.end(), soc) - (table.begin() + 1));
+        if (want != got) {
+            bad++;
+        }
+    }
+    // Also probe either side of every boundary, where a disagreement would actually hide
+    for (int32_t p = 1; p <= 100; p++) {
+        const double edge = table[p];
+        if (!std::isfinite(edge)) {
+            continue;
+        }
+        for (int32_t d = -2; d <= 2; d++) {
+            double probe = edge;
+            for (int32_t n = 0; n < std::abs(d); n++) {
+                probe = d < 0 ? std::nextafter(probe, -1.0) : std::nextafter(probe, 1e18);
+            }
+            if (probe < 0) {
+                continue;
+            }
+            const int32_t want = percent_via_round(probe, soc_max);
+            const int32_t got = static_cast<int32_t>(std::upper_bound(table.begin() + 1, table.end(), probe) - (table.begin() + 1));
+            if (want != got) {
+                bad++;
+            }
+        }
+    }
+    return bad;
+}
+
 } // extern "C"

From be2510d0c171d47a6d2bc9253a84f04ed8294e59 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 18:23:11 +0100
Subject: [PATCH 03/24] test(kernel): pin the SoC percent table, and rebuild
 the shipped binaries

Adds run_soc_percent_table_tests to the kernel parity suite. It drives the
kernel's own pk_verify_soc_percent_table over eleven battery sizes from 0.5 to
100 kWh, sweeping the SoC range and - the part that matters - probing either side
of all 101 bucket boundaries, since a boundary is the only place the table could
disagree with the round_py path it replaced.

Checked that the test actually bites: nudging a single boundary by one ULP makes
it fail on five of the eleven capacities, so it detects the smallest corruption
the table can have rather than only catching gross breakage.

Also rebuilds all six shipped binaries, which were built before the batch entry
point and the bucket table existed. Verified the new symbols are present in every
one (dynamic symbols for the four stripped Linux ELFs, nm for the two Mach-O), and
loaded the cross-built darwin_arm64 directly to confirm it reports the batch entry
point and agrees with round_py on the table. Without this a non-macOS install
would silently fall back to the Python engine.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .cspell/custom-dictionary-workspace.txt       |   2 ++
 apps/predbat/prediction_kernel_lib_aarch64.so | Bin 238832 -> 247200 bytes
 apps/predbat/prediction_kernel_lib_armv7l.so  | Bin 190808 -> 197840 bytes
 .../prediction_kernel_lib_darwin_arm64.so     | Bin 301208 -> 304200 bytes
 .../prediction_kernel_lib_darwin_x86_64.so    | Bin 262046 -> 268934 bytes
 apps/predbat/prediction_kernel_lib_i686.so    | Bin 220696 -> 229004 bytes
 apps/predbat/prediction_kernel_lib_x86_64.so  | Bin 248288 -> 257112 bytes
 apps/predbat/tests/test_kernel_parity.py      |  33 ++++++++++++++++++
 8 files changed, 35 insertions(+)

diff --git a/.cspell/custom-dictionary-workspace.txt b/.cspell/custom-dictionary-workspace.txt
index 1f59c0f7f..ee6ebdf9c 100644
--- a/.cspell/custom-dictionary-workspace.txt
+++ b/.cspell/custom-dictionary-workspace.txt
@@ -447,6 +447,7 @@ slugifies
 slugify
 slugifying
 snakeviz
+snprintf
 socb
 socketloop
 socs
@@ -472,6 +473,7 @@ stdlib
 stepline
 Stromligning
 strptime
+strtod
 Strømligning
 subclassing
 submessage
diff --git a/apps/predbat/prediction_kernel_lib_aarch64.so b/apps/predbat/prediction_kernel_lib_aarch64.so
index 2f989b60212c99a9838bc832ee51ce8fcb3a7dcf..8d654ad6f2569ce0fa3664fd2ad38ac27da7cddd 100755
GIT binary patch
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_arm64.so b/apps/predbat/prediction_kernel_lib_darwin_arm64.so
index 83a19542ce438db7964d1d886f9061a290a273f7..35ba000fc4bffa081a8cde44e7b8436d0e3f3fd7 100755
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_x86_64.so b/apps/predbat/prediction_kernel_lib_darwin_x86_64.so
index feb706535a7ed41248d198deac06a883b505aebe..2952311a6ad44107d23cc9bcffa7e53feeed77c7 100755
GIT binary patch
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diff --git a/apps/predbat/prediction_kernel_lib_i686.so b/apps/predbat/prediction_kernel_lib_i686.so
index f42ccbdefe4cb266a537450315b257fdd73110fc..f6a7582a750070ed1fcceef5369a5874839f6d99 100755
GIT binary patch
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diff --git a/apps/predbat/prediction_kernel_lib_x86_64.so b/apps/predbat/prediction_kernel_lib_x86_64.so
index 9eca7a3690db5d2838854b4e53d9ad7bc89a6e5a..216c2ab7f21fecfccc80ef789dae352e824b6553 100755
GIT binary patch
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diff --git a/apps/predbat/tests/test_kernel_parity.py b/apps/predbat/tests/test_kernel_parity.py
index 816c4c1c7..38bd9dbda 100644
--- a/apps/predbat/tests/test_kernel_parity.py
+++ b/apps/predbat/tests/test_kernel_parity.py
@@ -878,6 +878,38 @@ def run_model_kernel_tests(my_predbat):
         my_predbat.prediction_kernel_enable = False
 
 
+def run_soc_percent_table_tests():
+    """Check the kernel's SoC percent bucket table is exactly the round_py path it replaced.
+
+    The hot loop maps SoC to a curve percent through precomputed bucket boundaries instead of
+    round(soc, 1), which is only legitimate if the two agree for every SoC that can occur. The kernel
+    exposes pk_verify_soc_percent_table for this: it sweeps the SoC range and, more importantly,
+    probes either side of all 101 boundaries, which is the only place a disagreement could hide.
+
+    A failure here means plans would silently diverge from the Python engine at a bucket edge, so it
+    is checked across the range of battery sizes rather than one convenient value.
+    """
+    print("**** Running SoC percent table tests ****")
+    lib = prediction_kernel.KERNEL_LIB
+    if not lib or not hasattr(lib, "pk_verify_soc_percent_table"):
+        print("SKIP: kernel does not expose pk_verify_soc_percent_table")
+        return False
+
+    lib.pk_verify_soc_percent_table.restype = ctypes.c_int32
+    lib.pk_verify_soc_percent_table.argtypes = [ctypes.c_double, ctypes.c_int32]
+    failed = False
+    # Spread of real battery capacities, including awkward non-round ones where the percent
+    # boundaries do not line up with tidy SoC values
+    for soc_max in (0.5, 2.0, 4.8, 5.0, 9.5, 10.0, 13.5, 20.0, 25.6, 30.0, 100.0):
+        bad = lib.pk_verify_soc_percent_table(ctypes.c_double(soc_max), 200000)
+        if bad:
+            print("ERROR: SoC percent table disagrees with round_py in {} places for soc_max {}".format(bad, soc_max))
+            failed = True
+    if not failed:
+        print("SoC percent table matches round_py for every sampled SoC and every bucket boundary")
+    return failed
+
+
 def run_kernel_parity_tests(my_predbat):
     """Compare the C++ prediction kernel against the Python engine, returns True on failure"""
     print("**** Running kernel parity tests ****")
@@ -889,6 +921,7 @@ def run_kernel_parity_tests(my_predbat):
     state = snapshot_scenario_state(my_predbat)
     try:
         failed = run_marshalling_tests()
+        failed |= run_soc_percent_table_tests()
         failed |= run_edge_case_tests(my_predbat)
         if not failed:
             failed |= run_random_sweep_tests(my_predbat)

From f813d219fe716f96007d1bd5cf0093e77d41295e Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 18:25:05 +0100
Subject: [PATCH 04/24] fix(kernel): do not clamp the batch SoC range when no
 range was asked for

pk_run_one clamped soc_range_min/max whenever the out pointers were non-null, but
the batch path always passes them. A job that asks for no range (all_n, where
Python skips the scan) would therefore have come back with (soc_max, soc_max)
where thread_run_prediction_charge_min_max returns (soc_max, 0) - Python's
clamping lines sit inside the `if not all_n` branch, so they never run for that
case either.

Latent rather than live: nothing calls pk_run_batch from Python yet. Fixed before
wiring it up, since it would have surfaced as a plan difference confined to
all_n charge windows and been thoroughly unpleasant to track down.

pk_run is unaffected - it passes nullptr for both.

Full suite green including the slow tests (debug_cases, annual_integration,
load_ml), 101.22s.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/prediction_kernel.cpp            |  21 ++++++++++++------
 apps/predbat/prediction_kernel_lib_aarch64.so | Bin 247200 -> 247216 bytes
 apps/predbat/prediction_kernel_lib_armv7l.so  | Bin 197840 -> 197728 bytes
 .../prediction_kernel_lib_darwin_arm64.so     | Bin 304200 -> 304200 bytes
 .../prediction_kernel_lib_darwin_x86_64.so    | Bin 268934 -> 268934 bytes
 apps/predbat/prediction_kernel_lib_i686.so    | Bin 229004 -> 228908 bytes
 apps/predbat/prediction_kernel_lib_x86_64.so  | Bin 257112 -> 257112 bytes
 7 files changed, 14 insertions(+), 7 deletions(-)

diff --git a/apps/predbat/prediction_kernel.cpp b/apps/predbat/prediction_kernel.cpp
index 18d8840a5..79bc6c776 100644
--- a/apps/predbat/prediction_kernel.cpp
+++ b/apps/predbat/prediction_kernel.cpp
@@ -1250,13 +1250,20 @@ static int32_t pk_run_one(const ContextStore *store, const PkScenario *s, PkResu
     out->iboost_running_solar = iboost_running_solar;
     out->iboost_running_full = iboost_running_full;
     if (soc_range_min_out) {
-        // Mirrors the two clamping lines that follow the scan in
-        // thread_run_prediction_charge_min_max, so an empty range collapses to a single value
-        // rather than leaving min above max
-        const double clamped_max = soc_range_max > soc_range_min ? soc_range_max : soc_range_min;
-        const double clamped_min = soc_range_min < clamped_max ? soc_range_min : clamped_max;
-        *soc_range_min_out = clamped_min;
-        *soc_range_max_out = clamped_max;
+        if (want_soc_range) {
+            // Mirrors the two clamping lines that follow the scan in
+            // thread_run_prediction_charge_min_max, so an empty range collapses to a single value
+            // rather than leaving min above max
+            const double clamped_max = soc_range_max > soc_range_min ? soc_range_max : soc_range_min;
+            const double clamped_min = soc_range_min < clamped_max ? soc_range_min : clamped_max;
+            *soc_range_min_out = clamped_min;
+            *soc_range_max_out = clamped_max;
+        } else {
+            // No range asked for. Python's all_n path skips the scan AND the clamping, returning
+            // (soc_max, 0) untouched, so clamping here would hand back (soc_max, soc_max) instead.
+            *soc_range_min_out = c->soc_max;
+            *soc_range_max_out = 0.0;
+        }
     }
     return 0;
 }
diff --git a/apps/predbat/prediction_kernel_lib_aarch64.so b/apps/predbat/prediction_kernel_lib_aarch64.so
index 8d654ad6f2569ce0fa3664fd2ad38ac27da7cddd..90e1fd8ed70e4aa093223ffebb0e00edd529de70 100755
GIT binary patch
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diff --git a/apps/predbat/prediction_kernel_lib_armv7l.so b/apps/predbat/prediction_kernel_lib_armv7l.so
index 2b2c3aa57886216e4055fca1436461daaa3bbbf4..47f6f9624bc65544e941ceffe4f09651c261e2ad 100755
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_arm64.so b/apps/predbat/prediction_kernel_lib_darwin_arm64.so
index 35ba000fc4bffa081a8cde44e7b8436d0e3f3fd7..e422a1c7c753323c33afbb80a7ff2660854629ee 100755
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_x86_64.so b/apps/predbat/prediction_kernel_lib_darwin_x86_64.so
index 2952311a6ad44107d23cc9bcffa7e53feeed77c7..7ffdaf0309992a88b4fd6757d7c4568042e70630 100755
GIT binary patch
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diff --git a/apps/predbat/prediction_kernel_lib_i686.so b/apps/predbat/prediction_kernel_lib_i686.so
index f6a7582a750070ed1fcceef5369a5874839f6d99..b91da501480cf51e8842165e239d022ae04e7cfd 100755
GIT binary patch
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diff --git a/apps/predbat/prediction_kernel_lib_x86_64.so b/apps/predbat/prediction_kernel_lib_x86_64.so
index 216c2ab7f21fecfccc80ef789dae352e824b6553..609e1471e3da306f77b0cbe0504d525dc6c5bb1e 100755
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From 81ceaef303faf13ff4d8c20214504da3d611ac40 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 18:30:55 +0100
Subject: [PATCH 05/24] docs: hand-off notes for the Python batching refactor

Working notes so Phase B can be picked up cold: what the kernel work already
landed and what it measured, why Python is now 78% of plan time, the lazy-batch
design, the traps that cost time here (the export window mutation, round_py's
locale contention, test-fixture isolation), the verification protocol, and the
three approaches already measured and rejected so they are not tried again.

Delete before this branch merges.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 PHASE_B_BATCH_REFACTOR.md | 224 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 224 insertions(+)
 create mode 100644 PHASE_B_BATCH_REFACTOR.md

diff --git a/PHASE_B_BATCH_REFACTOR.md b/PHASE_B_BATCH_REFACTOR.md
new file mode 100644
index 000000000..35dd7dfb6
--- /dev/null
+++ b/PHASE_B_BATCH_REFACTOR.md
@@ -0,0 +1,224 @@
+# Phase B: batch the prediction fan-out from Python
+
+Working notes for picking this up in a fresh session. Delete this file before the branch merges.
+
+Branch: `perf/kernel-batch-abi` (4 commits, unpushed, based on `main`).
+
+## Where things stand
+
+The C++ side is done, verified and committed. The Python side has not been touched yet — it still
+issues one `pk_run` call per scenario.
+
+| commit | what |
+|---|---|
+| `a3254924` | `pk_run_batch` ABI added; stop filling the SoC series when the caller discards it |
+| `2e71022e` | precomputed SoC percent buckets replace `round_py` in the hot loop |
+| `be2510d0` | bucket table verification test; all 6 shipped binaries rebuilt |
+| `f813d219` | fix: don't clamp the batch SoC range when none was requested |
+
+Everything is byte-identical to before: all 20 random scenarios unchanged on metric, cost and all
+three PV futures, `kernel_parity` green, full suite green including slow tests (101.22s).
+
+### What the kernel work bought
+
+Measured on `cases/random_scenarios.yaml` scenario 0 (the benchmark scenario), `threads=0`:
+
+```
+calculate_plan   2418.4 ms  ->  1758.5 ms     -27%
+  C++ pk_run     1056.5 ms  ->   380.5 ms
+  Python         1361.9 ms  ->  1378.0 ms     unchanged, as expected
+C++ share            43.7%  ->     21.6%
+```
+
+20-scenario benchmark: **42.5s -> 31.6s**.
+
+## Why Phase B is now the whole game
+
+**Python is 78.4% of plan time.** The kernel is no longer the bottleneck by a wide margin — per
+prediction it is roughly 20us of C++ against ~70us of Python. Every further optimisation has to come
+off the Python side, and batching is the mechanism.
+
+There is a second, independent reason: `pk_run_batch` already takes an `n_threads` argument and
+scales properly now (**6.7x at 16 threads, bit-identical at every thread count**), but nothing can
+use it until Python submits work in batches. One `pk_run` call per scenario cannot be parallelised —
+that was measured and rejected twice, see "Dead ends" below.
+
+## The design
+
+### The call sites are already batch-shaped
+
+This is the key enabler and the reason this refactor is tractable. Every fan-out in `plan.py` was
+written for the process pool, so it already reads launch-all-then-collect-all:
+
+```python
+for try_soc in try_socs:
+    results.append(self.launch_run_prediction_charge(...))   # fan out
+for try_soc in try_socs:
+    resultmid[try_soc] = results.pop(0).get()                # then collect
+```
+
+`DummyThread` exists purely so the non-pooled path mimics that shape. So **the call sites do not need
+to change at all** — only what `launch_*` returns and when the work actually happens.
+
+### Lazy batch
+
+- `launch_run_prediction_*` stops running anything. It appends a job to a pending list on the
+  `Prediction` and returns a `BatchHandle(batch, index)`.
+- `BatchHandle.get()` flushes the whole pending batch on first call, then returns its own slot.
+  Subsequent `get()`s read already-computed results.
+- Flush: resolve prediction-cache hits in Python first, marshal only the misses into a
+  `PkBatchJob` array, one `pk_run_batch` call, then shape each job's result.
+
+Batch sizes this produces (scenario 0, from the profile):
+
+```
+launch_run_prediction_charge          10,698
+launch_run_prediction_export           5,436
+launch_run_prediction_single           4,752
+launch_run_prediction_charge_min_max   3,294
+                                      ------
+                                      24,180 launches, ~19,209 reaching the kernel
+```
+`optimise_charge_limit_price_threads` fans out ~1176 at a time (392 candidates x 3 PV scenarios),
+`optimise_charge_limit` ~900, `optimise_export` ~628. Those are the batches.
+
+### Do not duplicate the input preparation
+
+Each `thread_run_prediction_*` does three things: build the trial inputs, call `run_prediction`,
+shape the result. The batch path needs the first and third but not the second.
+
+**Split them rather than copying them**, so the direct and batch paths cannot drift:
+
+```python
+def _prepare_charge(self, try_soc, window_n, charge_limit, all_n):
+    """Build the trial charge limits - shared by thread_run_prediction_charge and the batch path."""
+    try_charge_limit = charge_limit.copy()
+    if all_n:
+        for set_n in all_n:
+            try_charge_limit[set_n] = try_soc
+    else:
+        try_charge_limit[window_n] = try_soc
+    return try_charge_limit
+```
+
+Same for export (`export_limits.copy()` plus the private window copy — see traps) and single
+(pass-through). Then `thread_run_prediction_*` becomes prepare + `run_prediction` + shape, and the
+batch enqueue is prepare + record.
+
+### min/max jobs
+
+`thread_run_prediction_charge_min_max` scans `predict_soc` across the charge window to get
+`min_soc`/`max_soc`. The kernel now does this itself so the batch does not have to ship an
+8928-entry SoC buffer per job (that would be ~84MB on a large batch). Set on the job:
+
+```python
+predict_minute_start = max(int((window["start"] - self.minutes_now) / 5) * 5, 0)
+predict_minute_end = int((window["end"] - self.minutes_now) / 5) * 5
+job.soc_range_start_step = predict_minute_start // 5
+job.soc_range_end_step = predict_minute_end // 5      # inclusive
+```
+
+and read `result.soc_range_min` / `result.soc_range_max` back.
+
+**When `all_n` is set, Python skips the scan and returns `(soc_max, 0)` unclamped.** Pass
+`soc_range_start_step = -1` for those; the kernel returns exactly `(soc_max, 0)` in that case (this
+is what `f813d219` fixed).
+
+### Fallbacks that must keep working
+
+The batch path only applies when the kernel can run the job. Fall back to running the job through
+the existing `thread_run_prediction_*` path, serially, when any of:
+
+- `KERNEL_HAS_BATCH` is False (binary predates `pk_run_batch` — the loader probes for the symbol)
+- `kernel_supported()` is False for the job (no kernel context, `debug_enable`, or a `save` run)
+- `pk_run_batch` returns non-zero, or a job's `status` field is non-zero
+
+A per-job `status` is returned precisely so one malformed scenario cannot discard the fan-out.
+
+## Traps found the hard way
+
+1. **`thread_run_prediction_export` used to mutate the caller's window dict** (`export_window[n]["start"] = start`).
+   That was only safe because each pool worker mutated its own unpickled copy. Any shared-memory
+   parallelism needs it local. The fix is on branch `perf/threadpool-prototype`:
+   ```python
+   export_window = list(export_window)
+   export_window[window_n] = dict(window, start=start)
+   ```
+   Only `["end"]` is ever read back by `optimise_export`, so nothing depends on the write being
+   visible. **Port this across.**
+
+2. **`round_py` is poison in any hot loop.** It is `snprintf`+`strtod`, ~96ns, and serialises on
+   libc's global locale — with constant work per thread it degrades **17x at 8 threads**. Two calls
+   remain (`iboost_next`, `car_soc_next`), both once per run, both harmless. Do not add more.
+
+3. **The prediction cache is worth keeping.** ~6,518 of 25,727 calls are hits (44%), saving far more
+   than the hashing costs. Resolve hits in Python before building the batch.
+
+4. **Test isolation.** Any new test that calls `calculate_plan` must not use the shared `my_predbat`
+   fixture — it arrives carrying whatever earlier tests left on it and `calculate_plan` raises. Follow
+   `run_annual_integration_isolated` / `run_window_cache_tests_isolated`: take a fresh instance from
+   `create_predbat()` and load a debug YAML.
+
+5. **`cd` in a Bash tool call does not persist**, and `git checkout` carries uncommitted changes
+   across branches. Both bit during this session.
+
+## Verification protocol
+
+Non-negotiable, in this order:
+
+```bash
+cd coverage && source setup.csh
+
+# 1. byte-identical plans - the real gate
+python3 ../apps/predbat/unit_test.py --random-run \
+    --random-template cases/predbat_debug_agile1.yaml \
+    --random-scenarios cases/random_scenarios.yaml
+python3 ../apps/predbat/unit_test.py --random-compare cases/random_results.json random_results.json
+# require: 20 unchanged, +0.0000 on metric, cost and all three PV futures
+
+# 2. kernel parity (includes the SoC bucket table check)
+./run_all --test kernel_parity
+
+# 3. full suite including slow tests
+./run_all
+
+# 4. quality gates
+./run_pre_commit        # re-run after the dictionary auto-sorts, then re-stage
+```
+
+Benchmark by summing `runtime_s` across the 20 scenarios, best of 3. Run-to-run spread is ~2-6%, so
+do not trust a single run — that mistake was made earlier in this work and produced a bogus 18%
+"win" that turned out to be noise.
+
+## Suggested order
+
+1. Port the `thread_run_prediction_export` mutation fix from `perf/threadpool-prototype`.
+2. Split `thread_run_prediction_*` into `_prepare_*` + shaping. **Verify parity here** — this step
+   alone should be a no-op.
+3. Add `BatchRunner` / `BatchHandle` and switch `launch_*` to enqueue. Keep `n_threads=1`.
+   **Verify parity.** This isolates the marshalling win with no concurrency risk.
+4. Turn on threading (`n_threads` from the `threads` config). **Verify parity again** — results were
+   bit-identical at every thread count in the C-level test, so any divergence here is a Python bug.
+5. Only then remove the process pool, `wrapped_run_prediction_*`, `PRED_GLOBAL` and `DummyThread`.
+
+## Dead ends - do not repeat these
+
+- **Python `ThreadPool` instead of the process pool.** `pk_run` does release the GIL (`ctypes.CDLL`)
+  and the kernel is reentrant, but per-call Python overhead makes the parallel fraction too small.
+  Peaks at **1.15x on two threads** then degrades below serial. Recorded with measurements on branch
+  `perf/threadpool-prototype` (commit message, marked do-not-merge).
+- **The process pool itself.** 1.11x on 16 cores; the parent's per-call pickling is a serial
+  bottleneck. Removing it costs almost nothing.
+- **Expecting the batch call itself to be faster.** `pk_run_batch` vs a loop of `pk_run` over 120
+  scenarios measured **0.99x**. The C boundary crossing was never the cost. The batch is worth doing
+  for the Python-side savings and because it is the only way to use threads — not for the call
+  itself.
+
+## Related work in flight
+
+`PR #4536` (`perf/window-bounds-cache`) caches window start/end arrays and the prediction-cache key
+tuple, worth **-21%** on its own and independent of this branch. It should compose: it removes Python
+per-call work, this removes C++ per-call work. If both land, re-profile before assuming what is left.
+
+Note that batching **partly subsumes** the window-bounds cache, since a batch ships window bounds once
+rather than per call. The `sim_hash` half still matters.

From 6693c3410be51388274e1049ef1fa7355ed36f21 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 18:50:51 +0100
Subject: [PATCH 06/24] docs: plan for batching the prediction fan-out from
 Python

Working plan for Phase B of the kernel batch work: launch_run_prediction_* queues jobs that a single
pk_run_batch call runs per fan-out, which is both where the remaining Python overhead goes and the
only shape that lets the kernel use threads.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .../2026-08-15-phase-b-batch-prediction.md    | 1589 +++++++++++++++++
 1 file changed, 1589 insertions(+)
 create mode 100644 docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md

diff --git a/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md b/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md
new file mode 100644
index 000000000..33406d018
--- /dev/null
+++ b/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md
@@ -0,0 +1,1589 @@
+# Phase B: Batch Prediction Fan-Out Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Make `launch_run_prediction_*` queue work into a lazily-flushed batch that reaches the C++ kernel through one `pk_run_batch` call per fan-out, so the per-call Python overhead is paid once per batch and the kernel's threading can finally be used.
+
+**Architecture:** The four `launch_run_prediction_*` call sites in `plan.py` are already launch-all-then-collect-all, so they do not change shape. `launch_*` stops running anything: it prepares the trial inputs, appends a `BatchJob` to a pending list on the `Prediction`, and returns a `BatchHandle`. The first `BatchHandle.get()` flushes the whole pending batch — prediction-cache hits are resolved in Python, the misses are marshalled into one `PkBatchJob` array and run by a single `pk_run_batch` call (optionally multi-threaded inside C++), and each job's result is then shaped into exactly what its `thread_run_prediction_*` used to return. Anything the kernel cannot take falls back to the existing Python path, per job.
+
+**Tech Stack:** Python 3 (ctypes), C++ prediction kernel (`prediction_kernel.cpp`, ABI 3, parity revision 5, already built and committed), predbat's own `unit_test.py` harness.
+
+**Spec:** [PHASE_B_BATCH_REFACTOR.md](../../../PHASE_B_BATCH_REFACTOR.md) — the working brief this plan implements. Read it first; it carries the measurements, the traps and the dead ends that justify the design below.
+
+## Global Constraints
+
+- **Branch:** `perf/kernel-batch-abi` (4 commits, unpushed, based on `main`). All work continues on this branch.
+- **No C++ changes.** The kernel side is done, verified and committed. If you find yourself editing `prediction_kernel.cpp`, stop — that means the design has drifted. (The one exception would be a genuine kernel bug, which then needs `PK_PARITY_REVISION` **and** `KERNEL_PARITY_REVISION` bumped and all 6 shipped binaries rebuilt.)
+- **Byte-identical plans are the gate.** Every task that can change results must end with the 20-scenario comparison showing `20 unchanged, +0.0000` on metric, cost and all three PV futures.
+- **Line length:** 256 chars (Black), 250 (Flake8).
+- **Docstrings:** 100% coverage required (`interrogate`) — every function, method and class, including `__init__`.
+- **Spelling:** British English (`en-gb`) via CSpell. New words go in `.cspell/custom-dictionary-workspace.txt`, which is auto-sorted on commit — re-stage after running pre-commit.
+- **Naming:** `lower_case_with_underscores`.
+- **Unit tests are mandatory for all new code** (CLAUDE.md), registered in `TEST_REGISTRY` in `apps/predbat/unit_test.py`.
+- **Test output goes to a file, then you grep the file.** Never pipe a test run straight into grep — a wrong pattern means running it all again.
+- **`cd` does not persist between Bash tool calls.** Every command below is written to be pasted whole.
+- **Do not `git checkout` between branches with uncommitted work** — it carries the changes across. Commit first.
+
+## Invariant this design depends on
+
+**No caller may mutate a list or window dict it has passed to `launch_run_prediction_*` between that call and the matching `.get()`.**
+
+Today every launch either runs immediately (serial path) or pickles its arguments immediately (pool path), so this invariant did not exist. Batching defers the read to flush time, so it does now. All four call sites were read and already satisfy it, *after* Task 1 removes the one violation:
+
+| call site | fan-out | why it is safe |
+|---|---|---|
+| `plan.py:528-530` (`optimise_levels`) | `launch_run_prediction_single` ×3 | `try_charge_limit`/`try_export` are freshly materialised per scenario and never written again |
+| `plan.py:1781-1799` (`optimise_charge_limit`) | `launch_run_prediction_charge_min_max` ×4-6 | `charge_limit`/`export_limits` are read-only across the block |
+| `plan.py:1950-1955` (`optimise_charge_limit`) | `launch_run_prediction_charge` ×2-3 per SoC | `try_charge_limit` is a separate list, only written in the results loop *after* every `.get()` |
+| `plan.py:2183-2186` (`optimise_export`) | `launch_run_prediction_export` ×2-3 per option | **violates it today** via `export_window[window_n]["start"] = start` — Task 1 fixes this |
+
+The 20-scenario byte-identical gate is what enforces the invariant in practice: any violation shows up as a changed plan immediately.
+
+## File Structure
+
+| File | Responsibility | Change |
+|---|---|---|
+| `apps/predbat/prediction_kernel.py` | ctypes/ABI layer only. Gains `BatchJob` (the queued-job record), `run_prediction_kernel_batch()` and `kernel_result_tuple()`; `pk_run_batch`'s argtypes get their missing `n_threads`. | Modify |
+| `apps/predbat/prediction_batch.py` | **New.** `PredictionBatch` mixin (pending list, enqueue, flush, per-job Python fallback), `BatchHandle`, `prediction_cache_key()`. Imports `prediction_kernel` and `const` only — never `prediction`, so there is no import cycle. | Create |
+| `apps/predbat/prediction.py` | `Prediction` gains the `PredictionBatch` mixin, the `_prepare_*` input builders shared by the direct and batch paths, `scan_soc_range()`, and the four `queue_run_prediction_*` entry points. | Modify |
+| `apps/predbat/plan.py` | The four `launch_run_prediction_*` become one-line delegations to `queue_run_prediction_*`; the process pool goes away. | Modify |
+| `apps/predbat/tests/test_kernel_parity.py` | Gains `run_batch_parity_tests()` — `pk_run_batch` against a loop of `pk_run`, at several thread counts. | Modify |
+| `apps/predbat/tests/test_prediction_batch.py` | **New.** The batch runner's behaviour: equivalence with the direct path for all four kinds, laziness, dedup, cache hits, and both fallback routes. | Create |
+| `apps/predbat/unit_test.py` | Register `prediction_batch` in `TEST_REGISTRY`. | Modify |
+| `docs/apps-yaml.md` | `threads` changes meaning: kernel threads, not worker processes. | Modify |
+
+---
+
+### Task 1: Stop the export trial mutating the caller's window
+
+**Files:**
+- Modify: `apps/predbat/prediction.py:358-373` (`thread_run_prediction_export`)
+- Test: `apps/predbat/tests/test_prediction_batch.py` (created here, one test; the rest of the file lands in Task 4)
+- Modify: `apps/predbat/unit_test.py` (register the new test)
+
+**Interfaces:**
+- Consumes: nothing from earlier tasks.
+- Produces: `thread_run_prediction_export` no longer writes to `export_window[window_n]["start"]`. Task 3 splits this same function, and Task 4 relies on the window list being safe to hold across a deferred flush.
+
+This is the fix from `perf/threadpool-prototype` (`263f8178`, marked do-not-merge), ported across. It was only ever safe because each pool worker mutated its own unpickled copy.
+
+- [ ] **Step 1: Write the failing test**
+
+Create `apps/predbat/tests/test_prediction_batch.py`:
+
+```python
+# -----------------------------------------------------------------------------
+# Predbat Home Battery System
+# Copyright Trefor Southwell 2026 - All Rights Reserved
+# This application maybe used for personal use only and not for commercial use
+# -----------------------------------------------------------------------------
+# fmt off
+# pylint: disable=consider-using-f-string
+# pylint: disable=line-too-long
+
+"""Tests for the batched prediction fan-out.
+
+launch_run_prediction_* no longer runs anything: it queues a job on the Prediction and returns a
+handle whose first get() flushes the whole batch through one pk_run_batch call. These tests pin the
+two things that makes conditional - that a queued job returns exactly what the direct
+thread_run_prediction_* path returns, and that a job the kernel cannot take still runs.
+"""
+
+import random
+
+from prediction import Prediction
+
+
+def make_export_windows(minutes_now):
+    """Build a small deterministic export window layout for the trial-input tests"""
+    return [
+        {"start": minutes_now + 60, "end": minutes_now + 120, "average": 15.0},
+        {"start": minutes_now + 180, "end": minutes_now + 240, "average": 20.0},
+    ]
+
+
+def test_export_trial_does_not_mutate_caller_window(my_predbat):
+    """The export trial must build its own window list, returns True on failure.
+
+    It used to write the trial start straight into the caller's window dict, which was only safe
+    because each pool worker mutated its own unpickled copy. A batched fan-out holds one shared list
+    across every job in the batch, so an in-place write would corrupt every other trial of the same
+    window.
+    """
+    print("**** Running export trial input tests ****")
+    failed = False
+    minutes_now = my_predbat.minutes_now
+    export_window = make_export_windows(minutes_now)
+    export_limits = [100.0, 100.0]
+    original = [dict(window) for window in export_window]
+
+    prediction = Prediction(my_predbat, {}, {}, {}, {})
+    trial_window, trial_limits = prediction._prepare_export(5.0, minutes_now + 90, 0, export_window, export_limits, None)
+
+    if export_window != original:
+        print("ERROR: _prepare_export mutated the caller's export window: {} vs {}".format(export_window, original))
+        failed = True
+    if export_limits != [100.0, 100.0]:
+        print("ERROR: _prepare_export mutated the caller's export limits: {}".format(export_limits))
+        failed = True
+    if trial_window[0]["start"] != minutes_now + 90:
+        print("ERROR: trial window start not applied, got {}".format(trial_window[0]["start"]))
+        failed = True
+    if trial_window[1] is not export_window[1]:
+        print("ERROR: untouched windows should be shared with the caller's list, not copied")
+        failed = True
+    if trial_limits[0] != 5.0:
+        print("ERROR: trial export limit not applied, got {}".format(trial_limits[0]))
+        failed = True
+
+    # The trial start is clamped to at least 5 minutes before the window end
+    trial_window, _ = prediction._prepare_export(5.0, minutes_now + 200, 0, export_window, export_limits, None)
+    if trial_window[0]["start"] != minutes_now + 115:
+        print("ERROR: trial window start not clamped to end-5, got {}".format(trial_window[0]["start"]))
+        failed = True
+
+    if not failed:
+        print("Export trial input tests passed")
+    return failed
+
+
+def run_prediction_batch_tests(my_predbat):
+    """Run every batched prediction test, returns True on failure"""
+    failed = test_export_trial_does_not_mutate_caller_window(my_predbat)
+    if failed:
+        print("**** Prediction batch tests FAILED ****")
+    else:
+        print("**** Prediction batch tests passed ****")
+    return failed
+```
+
+The test is written against `_prepare_export`, which does not exist yet — Step 4 of this task creates it, because extracting the helper *is* the fix: it is the only way to build the trial window without touching the caller's. Task 3 later joins the other three `thread_run_prediction_*` to the same pattern.
+
+- [ ] **Step 2: Register the test**
+
+In `apps/predbat/unit_test.py`, add the import next to the other test imports (they are alphabetical-ish, grouped by module):
+
+```python
+from tests.test_prediction_batch import run_prediction_batch_tests
+```
+
+and add the registry entry to `TEST_REGISTRY`, next to the other prediction entries:
+
+```python
+        ("prediction_batch", run_prediction_batch_tests, "Batched prediction fan-out tests", False),
+```
+
+- [ ] **Step 3: Run the test to verify it fails**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all --test prediction_batch > /tmp/predbat-task1.log 2>&1; grep -nE "ERROR|FAIL|PASS|Traceback|AttributeError" /tmp/predbat-task1.log | head -20
+```
+
+Expected: `AttributeError: 'Prediction' object has no attribute '_prepare_export'`.
+
+- [ ] **Step 4: Add `_prepare_export` and route the direct path through it**
+
+In `apps/predbat/prediction.py`, add the helper immediately above `thread_run_prediction_export`:
+
+```python
+    def _prepare_export(self, this_export_limit, start, window_n, export_window, export_limits, all_n):
+        """Build the trial export limits and window list - shared by thread_run_prediction_export and the batch path.
+
+        The trial start is applied to a private copy of the window rather than written into the
+        caller's list: with a process pool each worker mutated its own unpickled copy, but a batched
+        fan-out shares one list across every job in the batch, so an in-place write would corrupt the
+        other trials of the same window. Only ["end"] is ever read back by the caller
+        (optimise_export), so nothing depends on the write being visible.
+        """
+        export_limits = export_limits.copy()
+
+        if all_n:
+            for window_id in all_n:
+                export_limits[window_id] = this_export_limit
+        else:
+            export_limits[window_n] = this_export_limit
+            # Adjust start
+            window = export_window[window_n]
+            start = min(start, window["end"] - 5)
+            export_window = list(export_window)
+            export_window[window_n] = dict(window, start=start)
+
+        return export_window, export_limits
+```
+
+and replace the head of `thread_run_prediction_export` (everything from `# Store try value into the window` down to the line before the `(` of the result unpack) with:
+
+```python
+        export_window, export_limits = self._prepare_export(this_export_limit, start, window_n, export_window, export_limits, all_n)
+```
+
+- [ ] **Step 5: Run the test to verify it passes**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all --test prediction_batch > /tmp/predbat-task1.log 2>&1; grep -nE "ERROR|FAIL|passed|Traceback" /tmp/predbat-task1.log | head -20
+```
+
+Expected: `Export trial input tests passed`, no ERROR lines.
+
+- [ ] **Step 6: Verify plans are byte-identical**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && python3 ../apps/predbat/unit_test.py --random-run --random-template cases/predbat_debug_agile1.yaml --random-scenarios cases/random_scenarios.yaml > /tmp/predbat-task1-run.log 2>&1; python3 ../apps/predbat/unit_test.py --random-compare cases/random_results.json random_results.json > /tmp/predbat-task1-cmp.log 2>&1; tail -30 /tmp/predbat-task1-cmp.log
+```
+
+Expected: 20 scenarios unchanged, `+0.0000` on metric, cost, and all three PV futures. Anything else means the mutation *was* being depended on — stop and investigate before continuing.
+
+- [ ] **Step 7: Run the full suite**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all > /tmp/predbat-task1-all.log 2>&1; grep -nE "FAILED|ERROR|Traceback|All tests passed|tests passed" /tmp/predbat-task1-all.log | tail -20
+```
+
+- [ ] **Step 8: Commit**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && ./run_pre_commit && git add apps/predbat/prediction.py apps/predbat/tests/test_prediction_batch.py apps/predbat/unit_test.py && git commit -m "fix(prediction): build the export trial window privately instead of mutating the caller's
+
+The trial start was written straight into the caller's window dict, which was only safe because each
+pool worker mutated its own unpickled copy. A batched fan-out shares one window list across every job
+in the batch, so this has to be local. Ported from perf/threadpool-prototype.
+
+Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>"
+```
+
+(If pre-commit rewrites the cspell dictionary, `git add` it and re-run the commit.)
+
+---
+
+### Task 2: Batch entry point in the ctypes layer
+
+**Files:**
+- Modify: `apps/predbat/prediction_kernel.py:287-292` (argtypes), `:596-629` (result assembly)
+- Test: `apps/predbat/tests/test_kernel_parity.py`
+
+**Interfaces:**
+- Consumes: `PkBatchJob`, `PkBatchResult`, `KERNEL_HAS_BATCH` (already present on this branch).
+- Produces:
+  - `class BatchJob` — the queued-job record, with `__slots__` `("charge_limit", "charge_window", "export_window", "export_limits", "pv_scenario", "end_record", "step", "soc_range_start_step", "soc_range_end_step", "sim_hash", "want_range", "range_window", "result")`.
+  - `kernel_result_tuple(pred, result, predict_soc) -> tuple` — the 17-field `run_prediction()` result tuple built from a `PkResult`.
+  - `run_prediction_kernel_batch(pred, jobs, n_threads=1) -> list | None` — returns one `(result_tuple_or_None, soc_range_min, soc_range_max)` per job in job order, or `None` if the whole batch could not run.
+
+**The bug this task fixes first:** `pk_run_batch` is declared in C++ as
+`int32_t pk_run_batch(int64_t handle, const PkBatchJob *jobs, int32_t n_jobs, PkBatchResult *results, int32_t n_threads)`
+but `prediction_kernel.py:289` gives it only four `argtypes`. Nothing calls it yet so nothing has broken, but a call built against that binding hands the C++ side an undefined `n_threads`.
+
+- [ ] **Step 1: Write the failing test**
+
+Add to `apps/predbat/tests/test_kernel_parity.py`:
+
+```python
+def build_batch_jobs(my_predbat, rng, count):
+    """Build a list of BatchJob scenarios plus the Prediction they run against"""
+    apply_random_scenario(my_predbat, rng)
+    pv_step, pv10_step, load_step, load10_step = make_step_data(my_predbat, rng)
+    my_predbat.prediction_kernel_enable = True
+    prediction = Prediction(my_predbat, pv_step, pv10_step, load_step, load10_step)
+    prediction.kernel_handle = create_kernel_context(prediction)
+
+    charge_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, rng.randint(2, 5))
+    export_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, rng.randint(2, 5))
+    end_record = my_predbat.forecast_minutes
+
+    jobs = []
+    for index in range(count):
+        charge_limit = [round(rng.uniform(0, my_predbat.soc_max), 2) for _ in charge_window]
+        export_limits = [rng.choice([100.0, 99.0, 0.0, round(rng.uniform(1, 99), 1)]) for _ in export_window]
+        pv_scenario = rng.choice([PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10, PV_SCENARIO_PV90])
+        # Every third job asks for the SoC range over a charge window, as the min/max fan-out does
+        range_window = charge_window[index % len(charge_window)] if (index % 3) == 0 else None
+        start_step = -1
+        end_step = -1
+        if range_window is not None:
+            start_step = max(int((range_window["start"] - my_predbat.minutes_now) / 5) * 5, 0) // 5
+            end_step = int((range_window["end"] - my_predbat.minutes_now) / 5) * 5 // 5
+        jobs.append(
+            prediction_kernel.BatchJob(
+                charge_limit,
+                charge_window,
+                export_window,
+                export_limits,
+                pv_scenario,
+                end_record,
+                5,
+                soc_range_start_step=start_step,
+                soc_range_end_step=end_step,
+                want_range=range_window is not None,
+                range_window=range_window,
+            )
+        )
+    return prediction, jobs
+
+
+def run_batch_parity_tests(my_predbat, count=60):
+    """Check pk_run_batch matches a loop of pk_run exactly, at every thread count, returns True on failure.
+
+    The batch path is the only one Python will use after this refactor, and it is the only one that
+    can run scenarios concurrently, so it is checked against the single-scenario path it replaces
+    rather than against the Python engine (which run_random_sweep_tests already pins pk_run against).
+    Results must be bit-identical, not merely close: the whole point of the fan-out is that plans do
+    not change.
+    """
+    print("**** Running kernel batch parity tests ****")
+    if not prediction_kernel.KERNEL_HAS_BATCH:
+        print("SKIP: kernel does not expose pk_run_batch")
+        return False
+
+    failed = False
+    prediction, jobs = build_batch_jobs(my_predbat, random.Random(4321), count)
+    if not prediction.kernel_handle:
+        print("ERROR: batch parity kernel context creation failed")
+        return True
+
+    # Reference: one pk_run per scenario, with the SoC series materialised so the range can be
+    # scanned in Python exactly as thread_run_prediction_charge_min_max does
+    reference = []
+    for job in jobs:
+        single = run_prediction_kernel(prediction, job.charge_limit, job.charge_window, job.export_window, job.export_limits, job.pv_scenario, job.end_record, 5, False)
+        if single is None:
+            print("ERROR: reference pk_run failed")
+            return True
+        if job.range_window is None:
+            soc_range = (prediction.soc_max, 0)
+        else:
+            soc_range = prediction.scan_soc_range(single[11], job.range_window)
+        reference.append((single, soc_range))
+
+    for n_threads in (1, 2, 4, 8):
+        batched = prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads)
+        if batched is None:
+            print("ERROR: pk_run_batch failed at {} threads".format(n_threads))
+            return True
+        if len(batched) != len(jobs):
+            print("ERROR: pk_run_batch returned {} results for {} jobs".format(len(batched), len(jobs)))
+            return True
+        for index, (result_tuple, soc_range_min, soc_range_max) in enumerate(batched):
+            single, (expect_min, expect_max) = reference[index]
+            if result_tuple is None:
+                print("ERROR: job {} reported a non-zero status at {} threads".format(index, n_threads))
+                failed = True
+                continue
+            for field, name in enumerate(RESULT_NAMES):
+                if result_tuple[field] != single[field]:
+                    print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
+                    failed = True
+            # The batch never materialises the SoC series - that is what makes it affordable
+            if result_tuple[11] != {}:
+                print("ERROR: job {} returned a SoC series from the batch path".format(index))
+                failed = True
+            for field, name in [(12, "car_charging_soc_next"), (13, "iboost_next"), (14, "iboost_running"), (15, "iboost_running_solar"), (16, "iboost_running_full")]:
+                if result_tuple[field] != single[field]:
+                    print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
+                    failed = True
+            if soc_range_min != expect_min or soc_range_max != expect_max:
+                print("ERROR: job {} at {} threads SoC range {} {} expected {} {}".format(index, n_threads, soc_range_min, soc_range_max, expect_min, expect_max))
+                failed = True
+
+    if not failed:
+        print("Batch parity: {} scenarios bit-identical to pk_run at 1, 2, 4 and 8 threads".format(count))
+    return failed
+```
+
+and call it from `run_kernel_parity_tests`, after the clipping sweep:
+
+```python
+        if not failed:
+            failed |= run_batch_parity_tests(my_predbat)
+```
+
+The test uses `prediction.scan_soc_range`, which Task 3 creates. Add it in this task as part of Step 4 (Task 3 then routes `thread_run_prediction_charge_min_max` through it).
+
+- [ ] **Step 2: Run the test to verify it fails**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all --test kernel_parity > /tmp/predbat-task2.log 2>&1; grep -nE "ERROR|FAIL|Traceback|AttributeError|passed" /tmp/predbat-task2.log | head -20
+```
+
+Expected: `AttributeError: module 'prediction_kernel' has no attribute 'BatchJob'`.
+
+- [ ] **Step 3: Fix the `pk_run_batch` binding**
+
+In `apps/predbat/prediction_kernel.py`, replace the argtypes line inside the `try:` at line 288-289:
+
+```python
+                lib.pk_run_batch.restype = ctypes.c_int32
+                # The trailing c_int32 is n_threads - pk_run_batch takes five arguments, and ctypes
+                # will happily pass a fourth-and-a-bit if the binding says otherwise
+                lib.pk_run_batch.argtypes = [ctypes.c_int64, ctypes.POINTER(PkBatchJob), ctypes.c_int32, ctypes.POINTER(PkBatchResult), ctypes.c_int32]
+```
+
+- [ ] **Step 4: Add `BatchJob`, `kernel_result_tuple`, `run_prediction_kernel_batch` and `scan_soc_range`**
+
+In `apps/predbat/prediction_kernel.py`, add `BatchJob` after the `PkBatchResult` class:
+
+```python
+class BatchJob:
+    """One queued prediction: its trial inputs, how the result should be shaped, and the result itself.
+
+    run_prediction_kernel_batch reads only the input fields and the SoC range steps. sim_hash,
+    want_range, range_window and result belong to the batch runner in prediction_batch.py, which is
+    the only thing that creates these; they live here so the ctypes layer can be tested on its own.
+    """
+
+    __slots__ = ("charge_limit", "charge_window", "export_window", "export_limits", "pv_scenario", "end_record", "step", "soc_range_start_step", "soc_range_end_step", "sim_hash", "want_range", "range_window", "result")
+
+    def __init__(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step, soc_range_start_step=-1, soc_range_end_step=-1, sim_hash=None, want_range=False, range_window=None):
+        """Record one job's trial inputs; result stays None until the batch is flushed"""
+        self.charge_limit = charge_limit
+        self.charge_window = charge_window
+        self.export_window = export_window
+        self.export_limits = export_limits
+        self.pv_scenario = pv_scenario
+        self.end_record = end_record
+        self.step = step
+        self.soc_range_start_step = soc_range_start_step
+        self.soc_range_end_step = soc_range_end_step
+        self.sim_hash = sim_hash
+        self.want_range = want_range
+        self.range_window = range_window
+        self.result = None
+```
+
+Then factor the result assembly out of `run_prediction_kernel` so the single and batch paths cannot drift. Add:
+
+```python
+def kernel_result_tuple(pred, result, predict_soc):
+    """Assemble the 17-field Prediction.run_prediction() result tuple from a PkResult.
+
+    Shared by the single-scenario and batch paths - mirrors prediction.py:626-628, 1266-1284.
+    """
+    car_charging_soc_next = pred.car_charging_soc_next[:]
+    if result.car_soc_next_valid:
+        for car_n in range(pred.num_cars):
+            car_charging_soc_next[car_n] = result.car_soc_next[car_n]
+
+    iboost_next = result.iboost_next if pred.iboost_enable else pred.iboost_next
+
+    return (
+        round(result.final_metric, 4),
+        round(result.import_kwh_battery, 4),
+        round(result.import_kwh_house, 4),
+        round(result.export_kwh, 4),
+        round(result.soc_min, 4),
+        round(result.final_soc, 4),
+        result.soc_min_minute,
+        round(result.battery_cycle, 4),
+        round(result.metric_keep, 4),
+        round(result.final_iboost, 4),
+        round(result.final_carbon_g, 4),
+        predict_soc,
+        car_charging_soc_next,
+        iboost_next,
+        bool(result.iboost_running),
+        bool(result.iboost_running_solar),
+        bool(result.iboost_running_full),
+    )
+
+
+def reset_kernel_run_state(pred):
+    """Clear the per-run state attributes exactly as the Python engine does - prediction.py:414-422
+
+    Non-save runs never populate these, so they simply stay empty/False; a kernel run has to leave
+    the Prediction in the same state a Python run would.
+    """
+    pred.predict_soc_best = {}
+    pred.predict_metric_best = {}
+    pred.predict_iboost_best = {}
+    pred.predict_carbon_best = {}
+    pred.predict_clipped_best = {}
+    pred.iboost_running = False
+    pred.iboost_running_solar = False
+    pred.iboost_running_full = False
+
+
+def run_prediction_kernel_batch(pred, jobs, n_threads=1):
+    """Run a whole fan-out of prediction scenarios through the kernel in one pk_run_batch call.
+
+    Returns a list of (result_tuple, soc_range_min, soc_range_max) in job order, where result_tuple
+    is the same 17-field tuple Prediction.run_prediction() returns, or None for a job the kernel
+    refused - the caller re-runs those through the Python engine. Returns None when the batch could
+    not be run at all (no kernel, no batch entry point, or a non-zero return code).
+
+    Whether the kernel is allowed to answer at all is the caller's decision, not this function's:
+    kernel_supported() also refuses debug and saving runs, and flush_batch checks it before getting
+    here.
+
+    Batched jobs never materialise the per-minute SoC series: their callers all discard it, and one
+    buffer per job would be ~84MB on a large batch. The one thing a caller does want from it - the
+    SoC range across a charge window - the kernel tracks inline over
+    [soc_range_start_step, soc_range_end_step] instead.
+    """
+    lib = KERNEL_LIB
+    if not lib or not KERNEL_HAS_BATCH or not getattr(pred, "kernel_handle", 0):
+        return None
+
+    n_jobs = len(jobs)
+    job_array = (PkBatchJob * n_jobs)()
+    result_array = (PkBatchResult * n_jobs)()
+    # Every ctypes buffer behind a pointer field has to outlive the call. Structures reached through
+    # an array index do not keep their own _objects, so the buffers are held here rather than relying
+    # on ctypes' keepalive doing the right thing through the array.
+    buffers = []
+    # A fan-out reuses the same window lists across most of its jobs, so their start/end arrays are
+    # marshalled once per distinct list instead of once per job - that is the bulk of the batching
+    # win. Keyed on identity, with the list retained so an id() cannot be recycled mid-batch. This
+    # relies on no caller mutating a window list between enqueue and flush (see prediction_batch.py).
+    window_cache = {}
+
+    def window_arrays(windows):
+        """Marshal a window list's start/end arrays, reusing an earlier job's arrays where possible"""
+        entry = window_cache.get(id(windows))
+        if entry is None:
+            entry = (int32_array([window["start"] for window in windows]), int32_array([window["end"] for window in windows]), windows)
+            window_cache[id(windows)] = entry
+        return entry
+
+    for index, job in enumerate(jobs):
+        charge_start, charge_end, _ = window_arrays(job.charge_window)
+        export_start, export_end, _ = window_arrays(job.export_window)
+        charge_limit = double_array(job.charge_limit)
+        export_limits = double_array(job.export_limits)
+        buffers.append((charge_limit, export_limits))
+
+        pk_job = job_array[index]
+        pk_job.charge_limit = charge_limit
+        pk_job.charge_start = charge_start
+        pk_job.charge_end = charge_end
+        pk_job.export_limits = export_limits
+        pk_job.export_start = export_start
+        pk_job.export_end = export_end
+        pk_job.soc_out = None
+        pk_job.n_charge = len(job.charge_window)
+        pk_job.n_export = len(job.export_window)
+        pk_job.pv_scenario = int(job.pv_scenario)
+        pk_job.end_record = job.end_record
+        pk_job.step = PREDICT_STEP  # the caller's step is ignored - see run_prediction_kernel
+        pk_job.soc_range_start_step = job.soc_range_start_step
+        pk_job.soc_range_end_step = job.soc_range_end_step
+
+    return_code = lib.pk_run_batch(pred.kernel_handle, job_array, n_jobs, result_array, max(int(n_threads), 1))
+    if return_code != 0:
+        return None
+
+    reset_kernel_run_state(pred)
+
+    results = []
+    for index in range(n_jobs):
+        batch_result = result_array[index]
+        if batch_result.status != 0:
+            results.append((None, 0.0, 0.0))
+            continue
+        results.append((kernel_result_tuple(pred, batch_result.result, {}), batch_result.soc_range_min, batch_result.soc_range_max))
+    return results
+```
+
+Then rewrite the tail of `run_prediction_kernel` (from the `# Reset the per-run state attributes` comment to the end) to use the shared helpers:
+
+```python
+    reset_kernel_run_state(pred)
+
+    # Assemble the same return value as the Python engine
+    predict_soc = {}
+    if not cache:
+        # Indexed loop, not dict(zip(range(...), soc_out)): iterating a ctypes array boxes each double
+        # through the sequence protocol and measured 34% slower than subscripting it.
+        for k in range(n_steps):
+            predict_soc[k * PREDICT_STEP] = soc_out[k]
+
+    return kernel_result_tuple(pred, result, predict_soc)
+```
+
+Finally, add `scan_soc_range` to `Prediction` in `apps/predbat/prediction.py`, immediately above `thread_run_prediction_charge_min_max`:
+
+```python
+    def scan_soc_range(self, predict_soc, window):
+        """Return the (min, max) SoC across a charge window - shared by the direct and batch min/max paths.
+
+        The kernel computes the same range inline (see PkBatchJob.soc_range_start_step), so this is
+        only reached when a job falls back to the Python engine; the two must agree exactly, including
+        the clamping that collapses an empty range to a single value rather than leaving min above max.
+        """
+        min_soc = self.soc_max
+        max_soc = 0
+        predict_minute_start = max(int((window["start"] - self.minutes_now) / 5) * 5, 0)
+        predict_minute_end = int((window["end"] - self.minutes_now) / 5) * 5
+        for minute in range(predict_minute_start, predict_minute_end + 5, 5):
+            if minute in predict_soc:
+                min_soc = min(predict_soc[minute], min_soc)
+                max_soc = max(predict_soc[minute], max_soc)
+        max_soc = max(max_soc, min_soc)
+        min_soc = min(min_soc, max_soc)
+        return min_soc, max_soc
+```
+
+- [ ] **Step 5: Run the test to verify it passes**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all --test kernel_parity > /tmp/predbat-task2.log 2>&1; grep -nE "ERROR|FAIL|bit-identical|passed|Traceback" /tmp/predbat-task2.log | head -20
+```
+
+Expected: `Batch parity: 60 scenarios bit-identical to pk_run at 1, 2, 4 and 8 threads`, and the parity suite passing.
+
+- [ ] **Step 6: Verify the single-scenario path still matches (the refactor above touched it)**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all --test model_kernel --test kernel_parity > /tmp/predbat-task2-model.log 2>&1; grep -nE "ERROR|FAILED|passed" /tmp/predbat-task2-model.log | tail -20
+```
+
+- [ ] **Step 7: Commit**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && ./run_pre_commit && git add apps/predbat/prediction_kernel.py apps/predbat/prediction.py apps/predbat/tests/test_kernel_parity.py && git commit -m "feat(kernel): marshal a whole fan-out into one pk_run_batch call
+
+Adds BatchJob, run_prediction_kernel_batch and the shared result assembly, and gives pk_run_batch's
+ctypes binding the n_threads argument it was missing. Window start/end arrays are marshalled once per
+distinct window list rather than once per job, which is where the batching win comes from.
+
+Bit-identical to a loop of pk_run across 60 scenarios at 1, 2, 4 and 8 threads.
+
+Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+### Task 3: Split the trial-input building out of `thread_run_prediction_*`
+
+**Files:**
+- Modify: `apps/predbat/prediction.py:227-394` (all four `thread_run_prediction_*`)
+
+**Interfaces:**
+- Consumes: `_prepare_export` (Task 1), `scan_soc_range` (Task 2).
+- Produces:
+  - `_prepare_single(charge_limit, export_limits) -> (list, list)`
+  - `_prepare_charge(try_soc, window_n, charge_limit, all_n) -> list`
+  - `_prepare_export(...)` — already exists from Task 1.
+  Task 4's `queue_run_prediction_*` call exactly these, so the direct and batch paths cannot drift.
+
+This step must be a **no-op**. It exists so the batch path never gets a second copy of the input building.
+
+- [ ] **Step 1: Add the two remaining prepare helpers**
+
+In `apps/predbat/prediction.py`, above `thread_run_prediction_single`:
+
+```python
+    def _prepare_single(self, charge_limit, export_limits):
+        """Copy the caller's limit lists for a single-scenario trial - shared by thread_run_prediction_single and the batch path.
+
+        The copy used to live in Plan.launch_run_prediction_single, where it protected the caller's
+        lists from the pool pickling them; the batch path defers the read to flush time, which needs
+        the same protection, so it belongs with the other trial-input building.
+        """
+        return list(charge_limit), list(export_limits)
+
+    def _prepare_charge(self, try_soc, window_n, charge_limit, all_n):
+        """Build the trial charge limits - shared by thread_run_prediction_charge/_charge_min_max and the batch path"""
+        try_charge_limit = charge_limit.copy()
+        if all_n:
+            for set_n in all_n:
+                try_charge_limit[set_n] = try_soc
+        else:
+            try_charge_limit[window_n] = try_soc
+        return try_charge_limit
+```
+
+- [ ] **Step 2: Route the four thread functions through them**
+
+`thread_run_prediction_single` — insert as the first statement of the body:
+
+```python
+        charge_limit, export_limits = self._prepare_single(charge_limit, export_limits)
+```
+
+`thread_run_prediction_charge` and `thread_run_prediction_charge_min_max` — replace the five-line `try_charge_limit = charge_limit.copy()` block in each with:
+
+```python
+        try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
+```
+
+`thread_run_prediction_charge_min_max` — replace the scan block (`min_soc = self.soc_max` through `min_soc = min(min_soc, max_soc)`) with:
+
+```python
+        min_soc = self.soc_max
+        max_soc = 0
+        if not all_n:
+            min_soc, max_soc = self.scan_soc_range(predict_soc, charge_window[window_n])
+```
+
+`thread_run_prediction_export` — already routed through `_prepare_export` in Task 1.
+
+- [ ] **Step 3: Drop the now-duplicated copy in plan.py**
+
+`Plan.launch_run_prediction_single` in `apps/predbat/plan.py:675-676` still does its own `list()` copies. Delete those two lines — `_prepare_single` now owns that.
+
+- [ ] **Step 4: Verify the split changed nothing**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && python3 ../apps/predbat/unit_test.py --random-run --random-template cases/predbat_debug_agile1.yaml --random-scenarios cases/random_scenarios.yaml > /tmp/predbat-task3-run.log 2>&1; python3 ../apps/predbat/unit_test.py --random-compare cases/random_results.json random_results.json > /tmp/predbat-task3-cmp.log 2>&1; tail -30 /tmp/predbat-task3-cmp.log
+```
+
+Expected: 20 unchanged, `+0.0000` everywhere. This step alone should be a no-op — if it is not, the split is wrong.
+
+- [ ] **Step 5: Run the full suite**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all > /tmp/predbat-task3-all.log 2>&1; grep -nE "FAILED|ERROR|Traceback|tests passed" /tmp/predbat-task3-all.log | tail -20
+```
+
+- [ ] **Step 6: Commit**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && ./run_pre_commit && git add apps/predbat/prediction.py apps/predbat/plan.py && git commit -m "refactor(prediction): split trial-input building out of thread_run_prediction_*
+
+Each thread_run_prediction_* built the trial inputs, ran the prediction and shaped the result. The
+batch path needs the first and third but not the second, so they are split rather than copied - one
+definition of what a trial input is, for both paths.
+
+No-op: 20 random scenarios byte-identical.
+
+Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+### Task 4: The lazy batch runner, serial
+
+**Files:**
+- Create: `apps/predbat/prediction_batch.py`
+- Modify: `apps/predbat/prediction.py` (mixin, `queue_run_prediction_*`, `prediction_cache_key` reuse, `__init__`)
+- Modify: `apps/predbat/plan.py:671-714` (the four `launch_*`), `:27` (imports), `:1340-1365` (pool creation)
+- Test: `apps/predbat/tests/test_prediction_batch.py`
+
+**Interfaces:**
+- Consumes: `BatchJob`, `run_prediction_kernel_batch` (Task 2); `_prepare_single`, `_prepare_charge`, `_prepare_export`, `scan_soc_range` (Tasks 1 and 3).
+- Produces:
+  - `prediction_batch.prediction_cache_key(charge_limit, charge_window, export_limits, export_window, pv_scenario, end_record, step) -> int`
+  - `prediction_batch.BatchHandle` with `.get()` — the drop-in for `AsyncResult`/`DummyThread`.
+  - `prediction_batch.PredictionBatch` mixin: `enqueue_prediction(...) -> BatchHandle`, `flush_batch()`, `run_batch_job_python(job) -> tuple`, attributes `pending_batch` (list) and `batch_threads` (int).
+  - `Prediction.queue_run_prediction_single/_charge/_charge_min_max/_export` — same signatures as the matching `thread_run_prediction_*`, returning a `BatchHandle`.
+
+`batch_threads` stays at 1 for this whole task. That isolates the marshalling win with no concurrency in play, so any divergence here is a Python bug and nothing else.
+
+- [ ] **Step 1: Write the failing tests**
+
+Extend `apps/predbat/tests/test_prediction_batch.py` (keep the Task 1 test, add these). Import at the top:
+
+```python
+import prediction_batch
+from const import PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10
+from prediction_kernel import create_kernel_context
+from tests.test_kernel_parity import apply_random_scenario, kernel_available, make_step_data, make_windows, restore_scenario_state, snapshot_scenario_state
+```
+
+```python
+def make_batch_prediction(my_predbat, seed=7):
+    """Build a kernel-enabled Prediction plus a charge/export window layout to fan out over"""
+    rng = random.Random(seed)
+    apply_random_scenario(my_predbat, rng)
+    pv_step, pv10_step, load_step, load10_step = make_step_data(my_predbat, rng)
+    my_predbat.prediction_kernel_enable = True
+    prediction = Prediction(my_predbat, pv_step, pv10_step, load_step, load10_step)
+    prediction.kernel_handle = create_kernel_context(prediction)
+    charge_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, 4)
+    export_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, 4)
+    charge_limit = [round(my_predbat.soc_max / 2, 2)] * len(charge_window)
+    export_limits = [100.0] * len(export_window)
+    return prediction, charge_window, export_window, charge_limit, export_limits
+
+
+def test_queued_matches_direct(my_predbat):
+    """Every queued prediction must return exactly what the direct thread path returns, returns True on failure"""
+    print("**** Running queued vs direct equivalence tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+    try_soc = round(my_predbat.soc_max * 0.8, 2)
+
+    cases = [
+        ("single", lambda: prediction.thread_run_prediction_single(charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, end_record, 5), lambda: prediction.queue_run_prediction_single(charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, end_record, 5)),
+        ("charge", lambda: prediction.thread_run_prediction_charge(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_PV10, None, end_record), lambda: prediction.queue_run_prediction_charge(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_PV10, None, end_record)),
+        ("charge_min_max", lambda: prediction.thread_run_prediction_charge_min_max(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record), lambda: prediction.queue_run_prediction_charge_min_max(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record)),
+        ("charge_min_max_all_n", lambda: prediction.thread_run_prediction_charge_min_max(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, [0, 2], end_record), lambda: prediction.queue_run_prediction_charge_min_max(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, [0, 2], end_record)),
+        ("export", lambda: prediction.thread_run_prediction_export(5.0, export_window[1]["start"] + 15, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record), lambda: prediction.queue_run_prediction_export(5.0, export_window[1]["start"] + 15, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record)),
+    ]
+
+    for name, direct_call, queue_call in cases:
+        # The queued run goes first, so the direct run cannot be answered out of the cache the queued
+        # one just populated - and vice versa, hence the clear between them
+        prediction.prediction_cache = {}
+        queued = queue_call().get()
+        prediction.prediction_cache = {}
+        direct = direct_call()
+        if queued != direct:
+            print("ERROR: {} queued {} != direct {}".format(name, queued, direct))
+            failed = True
+
+    if not failed:
+        print("Queued predictions match the direct path for all five shapes")
+    return failed
+
+
+def test_batch_is_lazy(my_predbat):
+    """Nothing runs until the first get(), and one get() flushes the whole batch, returns True on failure"""
+    print("**** Running batch laziness tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+
+    handles = [prediction.queue_run_prediction_charge(round(soc, 2), 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record) for soc in (1.0, 2.0, 3.0)]
+    if len(prediction.pending_batch) != 3:
+        print("ERROR: expected 3 pending jobs, got {}".format(len(prediction.pending_batch)))
+        failed = True
+    if any(handle.job.result is not None for handle in handles):
+        print("ERROR: a job ran before its handle was read")
+        failed = True
+
+    handles[0].get()
+    if prediction.pending_batch:
+        print("ERROR: pending batch not drained by the first get(), {} left".format(len(prediction.pending_batch)))
+        failed = True
+    if any(handle.job.result is None for handle in handles):
+        print("ERROR: one get() did not flush the whole batch")
+        failed = True
+
+    # A second read must not re-run anything
+    before = handles[2].job.result
+    if handles[2].get() is not before:
+        print("ERROR: reading a handle twice recomputed the result")
+        failed = True
+
+    if not failed:
+        print("Batch laziness tests passed")
+    return failed
+
+
+def test_batch_cache_and_dedup(my_predbat):
+    """Cache hits and intra-batch duplicates must never reach the kernel, returns True on failure"""
+    print("**** Running batch cache/dedup tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+    prediction.prediction_cache_enable = True
+
+    calls = []
+    real_batch = prediction_batch.run_prediction_kernel_batch
+
+    def spy(pred, jobs, n_threads):
+        """Record how many jobs each batch call was handed, then run it for real"""
+        calls.append(len(jobs))
+        return real_batch(pred, jobs, n_threads)
+
+    prediction_batch.run_prediction_kernel_batch = spy
+    try:
+        # Three identical jobs in one batch must collapse to one kernel job
+        prediction.prediction_cache = {}
+        handles = [prediction.queue_run_prediction_charge(4.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record) for _ in range(3)]
+        results = [handle.get() for handle in handles]
+        if calls != [1]:
+            print("ERROR: expected one kernel job for three identical trials, got {}".format(calls))
+            failed = True
+        if results[0] != results[1] or results[1] != results[2]:
+            print("ERROR: deduplicated jobs returned different results")
+            failed = True
+
+        # And a repeat in a later batch must come from the cache without a kernel call at all
+        del calls[:]
+        again = prediction.queue_run_prediction_charge(4.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+        if calls:
+            print("ERROR: a cached scenario still reached the kernel: {}".format(calls))
+            failed = True
+        if again != results[0]:
+            print("ERROR: cached result {} differs from the computed one {}".format(again, results[0]))
+            failed = True
+    finally:
+        prediction_batch.run_prediction_kernel_batch = real_batch
+
+    if not failed:
+        print("Batch cache/dedup tests passed")
+    return failed
+
+
+def test_batch_fallbacks(my_predbat):
+    """A job the kernel cannot take still runs, and gives the Python engine's answer, returns True on failure"""
+    print("**** Running batch fallback tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+
+    # Two references, because the fallbacks land on two different engines. A batch the kernel refused
+    # still re-runs through run_prediction, which dispatches to the kernel; a Prediction the kernel
+    # will not serve at all re-runs on the Python engine. Those two agree to 1e-6 (kernel_parity), not
+    # bit-exactly, so each fallback is compared against the engine it actually used.
+    prediction.prediction_cache = {}
+    expected = prediction.thread_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record)
+    prediction.debug_enable = True
+    prediction.prediction_cache = {}
+    expected_python = prediction.thread_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record)
+    prediction.debug_enable = False
+
+    # 1. Whole batch refused (no kernel, stale binary, or a non-zero return code)
+    real_batch = prediction_batch.run_prediction_kernel_batch
+
+    def refuse_all(pred, jobs, n_threads):
+        """Stand in for a kernel that cannot run the batch at all"""
+        return None
+
+    def refuse_each(pred, jobs, n_threads):
+        """Stand in for a kernel that ran the batch but rejected every job"""
+        return [(None, 0.0, 0.0)] * len(jobs)
+
+    for name, stub in (("whole batch", refuse_all), ("per job", refuse_each)):
+        prediction_batch.run_prediction_kernel_batch = stub
+        try:
+            prediction.prediction_cache = {}
+            got = prediction.queue_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+            if got != expected:
+                print("ERROR: {} fallback returned {} expected {}".format(name, got, expected))
+                failed = True
+        finally:
+            prediction_batch.run_prediction_kernel_batch = real_batch
+
+    # 2. Debug runs must not reach the kernel from the batch when the direct path would not take it
+    prediction.debug_enable = True
+    try:
+        prediction.prediction_cache = {}
+        got = prediction.queue_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+        if got != expected_python:
+            print("ERROR: debug_enable fallback returned {} expected {}".format(got, expected_python))
+            failed = True
+    finally:
+        prediction.debug_enable = False
+
+    # 3. Kernel genuinely unavailable for this Prediction
+    prediction.kernel_handle = 0
+    prediction.prediction_cache = {}
+    got = prediction.queue_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+    if got != expected_python:
+        print("ERROR: kernel-less fallback returned {} expected {}".format(got, expected_python))
+        failed = True
+
+    if not failed:
+        print("Batch fallback tests passed")
+    return failed
+```
+
+and rewrite the runner at the bottom of the file:
+
+```python
+def run_prediction_batch_tests(my_predbat):
+    """Run every batched prediction test, returns True on failure"""
+    failed = test_export_trial_does_not_mutate_caller_window(my_predbat)
+
+    available, required_failure = kernel_available()
+    if not available:
+        print("WARNING: kernel not available - batch tests that need it are SKIPPED")
+        return failed or required_failure
+
+    state = snapshot_scenario_state(my_predbat)
+    try:
+        failed |= test_queued_matches_direct(my_predbat)
+        failed |= test_batch_is_lazy(my_predbat)
+        failed |= test_batch_cache_and_dedup(my_predbat)
+        failed |= test_batch_fallbacks(my_predbat)
+    finally:
+        restore_scenario_state(my_predbat, state)
+        my_predbat.prediction_kernel_enable = False
+
+    if failed:
+        print("**** Prediction batch tests FAILED ****")
+    else:
+        print("**** Prediction batch tests passed ****")
+    return failed
+```
+
+- [ ] **Step 2: Run the tests to verify they fail**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all --test prediction_batch > /tmp/predbat-task4.log 2>&1; grep -nE "ERROR|FAIL|Traceback|ModuleNotFound|AttributeError" /tmp/predbat-task4.log | head -20
+```
+
+Expected: `ModuleNotFoundError: No module named 'prediction_batch'`.
+
+- [ ] **Step 3: Write `prediction_batch.py`**
+
+```python
+# -----------------------------------------------------------------------------
+# Predbat Home Battery System
+# Copyright Trefor Southwell 2026 - All Rights Reserved
+# This application maybe used for personal use only and not for commercial use
+# -----------------------------------------------------------------------------
+# fmt off
+# pylint: disable=consider-using-f-string
+# pylint: disable=line-too-long
+
+"""Lazy batching of the prediction fan-out.
+
+Every optimiser fan-out in plan.py is launch-all-then-collect-all, so the launches can queue instead
+of running: each one prepares its trial inputs, appends a BatchJob and returns a BatchHandle, and the
+first get() runs the whole batch through a single pk_run_batch call. That pays the Python/C boundary
+and the window marshalling once per fan-out instead of once per scenario, and it is the only shape
+that lets the kernel spread the work across cores - a Python thread pool cannot (the GIL serialises
+it) and a process pool cannot (the parent's pickling is a serial bottleneck); both were measured and
+rejected.
+
+INVARIANT: a caller must not mutate any list or window dict it has handed to a queue_* entry point
+until it has read the matching handle. The four fan-outs in plan.py satisfy this; the 20-scenario
+byte-identical plan comparison is what keeps it that way.
+"""
+
+from operator import itemgetter
+
+from const import PREDICT_STEP
+from prediction_kernel import BatchJob, kernel_supported, run_prediction_kernel_batch
+
+# Pulls (start, end) from a window dict; used to build the prediction cache key without a Python
+# level loop over every window
+window_bounds = itemgetter("start", "end")
+
+
+def prediction_cache_key(charge_limit, charge_window, export_limits, export_window, pv_scenario, end_record, step):
+    """Hash a simulation's inputs into its prediction cache key.
+
+    Shared by Prediction.run_prediction and the batch runner so a scenario cannot land under two
+    different keys depending on which path reached it. Built as one tuple hash to keep the per-window
+    hashing in C rather than looping in Python, which matters because it runs on every simulation
+    with a few hundred windows.
+    """
+    return hash(
+        (
+            tuple(charge_limit),
+            tuple(map(window_bounds, charge_window)),
+            tuple(export_limits),
+            tuple(map(window_bounds, export_window)),
+            pv_scenario,
+            end_record,
+            step,
+        )
+    )
+
+
+def shape_batch_result(job, result, soc_range_min, soc_range_max):
+    """Shape a full run_prediction result into what this job's thread_run_prediction_* returned"""
+    shaped = tuple(result[:11])
+    if job.want_range:
+        shaped = shaped + (soc_range_min, soc_range_max)
+    return shaped
+
+
+class BatchHandle:
+    """Result handle for a queued prediction - the drop-in for the pool's AsyncResult"""
+
+    __slots__ = ("pred", "job")
+
+    def __init__(self, pred, job):
+        """Bind the handle to the Prediction that owns the batch and to its own job"""
+        self.pred = pred
+        self.job = job
+
+    def get(self):
+        """Return this job's result, flushing the whole pending batch on the first call"""
+        if self.job.result is None:
+            self.pred.flush_batch()
+        return self.job.result
+
+
+class PredictionBatch:
+    """Pending-batch state and flushing for Prediction.
+
+    A mixin rather than a separate object so the pending list lives directly on the Prediction that
+    owns the kernel context, with no reference cycle between the two.
+    """
+
+    def enqueue_prediction(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step, cache, want_range=False, range_window=None):
+        """Queue one prediction and return a handle to its eventual result"""
+        sim_hash = prediction_cache_key(charge_limit, charge_window, export_limits, export_window, pv_scenario, end_record, step) if cache else None
+
+        # The kernel tracks the SoC range itself over a step range, so a min/max job does not have to
+        # ship an 8928-entry SoC buffer. A negative start step means "no range asked for", which the
+        # kernel answers with (soc_max, 0) - exactly what the Python all_n path returns unclamped.
+        soc_range_start_step = -1
+        soc_range_end_step = -1
+        if range_window is not None:
+            predict_minute_start = max(int((range_window["start"] - self.minutes_now) / 5) * 5, 0)
+            predict_minute_end = int((range_window["end"] - self.minutes_now) / 5) * 5
+            soc_range_start_step = predict_minute_start // PREDICT_STEP
+            soc_range_end_step = predict_minute_end // PREDICT_STEP
+
+        job = BatchJob(
+            charge_limit,
+            charge_window,
+            export_window,
+            export_limits,
+            pv_scenario,
+            end_record,
+            step,
+            soc_range_start_step=soc_range_start_step,
+            soc_range_end_step=soc_range_end_step,
+            sim_hash=sim_hash,
+            want_range=want_range,
+            range_window=range_window,
+        )
+        self.pending_batch.append(job)
+        return BatchHandle(self, job)
+
+    def run_batch_job_python(self, job):
+        """Run one job through the normal prediction path, for when the kernel cannot take it"""
+        result = self.run_prediction(job.charge_limit, job.charge_window, job.export_window, job.export_limits, job.pv_scenario, end_record=job.end_record, step=job.step, cache=job.sim_hash is not None)
+        if not job.want_range:
+            return tuple(result[:11])
+        if job.range_window is None:
+            return tuple(result[:11]) + (self.soc_max, 0)
+        return tuple(result[:11]) + self.scan_soc_range(result[11], job.range_window)
+
+    def flush_batch(self):
+        """Run every pending job: cache hits first, then one kernel call, then any fallbacks"""
+        pending = self.pending_batch
+        if not pending:
+            return
+        # Cleared before anything runs, so a fallback that re-enters run_prediction cannot see a
+        # half-flushed batch or flush it a second time
+        self.pending_batch = []
+
+        cache = self.prediction_cache
+        kernel_jobs = []
+        duplicates = []
+        first_by_hash = {}
+        for job in pending:
+            if job.sim_hash is not None:
+                cached = cache.get(job.sim_hash)
+                if cached is not None:
+                    job.result = shape_batch_result(job, cached, 0.0, 0.0)
+                    continue
+                # Two identical trials in one fan-out used to be a cache hit for the second one; the
+                # batch has to collapse them itself or that hit is lost (~44% of calls are hits)
+                first = first_by_hash.get(job.sim_hash)
+                if first is not None:
+                    duplicates.append((job, first))
+                    continue
+                first_by_hash[job.sim_hash] = job
+            kernel_jobs.append(job)
+
+        if kernel_jobs:
+            # kernel_supported() is a property of the Prediction, not of the individual job - it also
+            # refuses debug runs, which would otherwise reach the kernel here when the direct path
+            # would have used the Python engine. save is always None for a queued job: a saving run
+            # calls run_prediction directly and never fans out.
+            batched = run_prediction_kernel_batch(self, kernel_jobs, self.batch_threads) if kernel_supported(self, None, PREDICT_STEP) else None
+            for index, job in enumerate(kernel_jobs):
+                entry = batched[index] if batched is not None else None
+                if entry is None or entry[0] is None:
+                    job.result = self.run_batch_job_python(job)
+                    continue
+                result, soc_range_min, soc_range_max = entry
+                if job.sim_hash is not None:
+                    # Stored without the SoC/car data to save memory, mirroring run_prediction
+                    cache[job.sim_hash] = result[:11] + ([], []) + result[13:]
+                job.result = shape_batch_result(job, result, soc_range_min, soc_range_max)
+
+        for job, first in duplicates:
+            job.result = first.result
+```
+
+- [ ] **Step 4: Wire the mixin and the queue entry points into `Prediction`**
+
+In `apps/predbat/prediction.py`, replace the local `window_bounds` definition and the inline hash in `run_prediction` with the shared ones, and add the mixin. Imports:
+
+```python
+from prediction_batch import PredictionBatch, prediction_cache_key
+```
+
+Delete the module-level `window_bounds = itemgetter("start", "end")` and its comment (and the now-unused `from operator import itemgetter` if nothing else uses it — check first).
+
+Class declaration:
+
+```python
+class Prediction(PredictionBatch):
+```
+
+In `__init__`, after the `PRED_GLOBAL["dict"] = self.__dict__.copy()` line:
+
+```python
+            # Deliberately after the PRED_GLOBAL snapshot: a pool worker reconstructs its Prediction
+            # from that dict and never queues anything, so it has no business carrying a batch.
+            self.pending_batch = []
+            self.batch_threads = 1
+```
+
+In `run_prediction`, replace the inline `sim_hash = hash((...))` block with:
+
+```python
+        sim_hash = None
+        if cache and not save:
+            sim_hash = prediction_cache_key(charge_limit, charge_window, export_limits, export_window, pv_scenario, end_record, step)
+            cached_result = self.prediction_cache.get(sim_hash)
+            if cached_result is not None:
+                # Return cached result
+                return cached_result
+```
+
+Then add the four queue entry points, next to their `thread_run_prediction_*` twins:
+
+```python
+    def queue_run_prediction_single(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step):
+        """Queue a single-scenario prediction, returning a handle - the batch runs on the first get()"""
+        charge_limit, export_limits = self._prepare_single(charge_limit, export_limits)
+        return self.enqueue_prediction(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step, self.prediction_cache_enable)
+
+    def queue_run_prediction_charge(self, try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
+        """Queue a charge-window trial prediction, returning a handle"""
+        try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
+        return self.enqueue_prediction(try_charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, PREDICT_STEP, self.prediction_cache_enable)
+
+    def queue_run_prediction_charge_min_max(self, try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
+        """Queue a charge-window trial prediction that also reports the SoC range across that window.
+
+        Uncached, exactly as the direct path is: the SoC range is not part of the cached result, so a
+        hit would answer with the wrong shape.
+        """
+        try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
+        range_window = None if all_n else charge_window[window_n]
+        return self.enqueue_prediction(try_charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, PREDICT_STEP, False, want_range=True, range_window=range_window)
+
+    def queue_run_prediction_export(self, this_export_limit, start, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
+        """Queue an export-window trial prediction, returning a handle"""
+        export_window, export_limits = self._prepare_export(this_export_limit, start, window_n, export_window, export_limits, all_n)
+        return self.enqueue_prediction(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, PREDICT_STEP, self.prediction_cache_enable)
+```
+
+- [ ] **Step 5: Switch the launch sites and stop creating the pool**
+
+In `apps/predbat/plan.py`, replace all four `launch_run_prediction_*` bodies:
+
+```python
+    def launch_run_prediction_single(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step=PREDICT_STEP):
+        """Queue a prediction and return a handle to its result"""
+        return self.prediction.queue_run_prediction_single(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step)
+
+    def launch_run_prediction_charge(self, loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
+        """Queue a prediction and return a handle to its result"""
+        return self.prediction.queue_run_prediction_charge(loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record)
+
+    def launch_run_prediction_charge_min_max(self, loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
+        """Queue a prediction and return a handle to its result"""
+        return self.prediction.queue_run_prediction_charge_min_max(loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record)
+
+    def launch_run_prediction_export(self, this_export_limit, start, window_n, try_charge_limit, charge_window, try_export_window, try_export, pv_scenario, all_n, end_record):
+        """Queue a prediction and return a handle to its result"""
+        return self.prediction.queue_run_prediction_export(this_export_limit, start, window_n, try_charge_limit, charge_window, try_export_window, try_export, pv_scenario, all_n, end_record)
+```
+
+Delete the `wrapped_run_prediction_*` import (line 27 becomes `from prediction import Prediction`) and the `from multiprocessing import Pool, cpu_count` import — both are unused now and Flake8 will fail otherwise.
+
+Delete the pool creation block (`# Create pool` through the `self.log("Not using threading...")` line) and the LoadML pool-disable block above it, keeping the LoadML status log:
+
+```python
+        # Check if LoadML is active - it used to force the process pool off, which no longer exists;
+        # the kernel's threads are C++ threads with no fork and no NumPy involvement
+        load_ml_comp = self.components.get_component("load_ml") if self.components else None
+        if load_ml_comp:
+            self.log("LoadML is_calculating {}".format(load_ml_comp.is_calculating()))
+
+        # Serial for now - Task 5 wires this to the threads config
+        self.prediction.batch_threads = 1
+```
+
+Place that `batch_threads` assignment immediately after `self.prediction = Prediction(...)` at line 1336.
+
+- [ ] **Step 6: Run the new tests to verify they pass**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all --test prediction_batch --test kernel_parity > /tmp/predbat-task4.log 2>&1; grep -nE "ERROR|FAIL|passed|Traceback" /tmp/predbat-task4.log | head -30
+```
+
+- [ ] **Step 7: Verify plans are byte-identical — this is the real gate**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && python3 ../apps/predbat/unit_test.py --random-run --random-template cases/predbat_debug_agile1.yaml --random-scenarios cases/random_scenarios.yaml > /tmp/predbat-task4-run.log 2>&1; python3 ../apps/predbat/unit_test.py --random-compare cases/random_results.json random_results.json > /tmp/predbat-task4-cmp.log 2>&1; tail -30 /tmp/predbat-task4-cmp.log
+```
+
+Expected: 20 unchanged, `+0.0000` on metric, cost and all three PV futures. If a scenario moves, the most likely causes in order are: a caller mutating an input between launch and get (the invariant above), the min/max range steps disagreeing with the Python scan, or a cache key built differently on the two paths.
+
+- [ ] **Step 8: Run the full suite including the slow tests**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all > /tmp/predbat-task4-all.log 2>&1; grep -nE "FAILED|ERROR|Traceback|tests passed|seconds" /tmp/predbat-task4-all.log | tail -25
+```
+
+- [ ] **Step 9: Record the serial benchmark**
+
+Sum `runtime_s` across the 20 scenarios, best of 3 runs. Run-to-run spread is 2-6%, so a single run proves nothing — an earlier single-run measurement in this work produced a bogus 18% "win" that was pure noise.
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && for i in 1 2 3; do python3 ../apps/predbat/unit_test.py --random-run --random-template cases/predbat_debug_agile1.yaml --random-scenarios cases/random_scenarios.yaml > /tmp/predbat-bench-serial-$i.log 2>&1; python3 -c "import json;print(round(sum(s['runtime_s'] for s in json.load(open('random_results.json'))['results']), 2))"; done
+```
+
+Baseline to beat: **31.6s**.
+
+- [ ] **Step 10: Commit**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && ./run_pre_commit && git add apps/predbat/prediction_batch.py apps/predbat/prediction.py apps/predbat/plan.py apps/predbat/tests/test_prediction_batch.py && git commit -m "perf(plan): batch the prediction fan-out into one kernel call
+
+launch_run_prediction_* no longer runs anything: it prepares the trial inputs, queues a job and
+returns a handle whose first get() flushes the whole batch through a single pk_run_batch call. Window
+start/end arrays are marshalled once per fan-out rather than once per scenario, cache hits and
+intra-batch duplicates are resolved in Python first, and anything the kernel refuses falls back to the
+Python engine per job. Serial for now.
+
+20 random scenarios byte-identical.
+
+Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+### Task 5: Turn on kernel threading
+
+**Files:**
+- Modify: `apps/predbat/plan.py` (threads config → `batch_threads`)
+- Modify: `docs/apps-yaml.md:285-297` (`threads` now means kernel threads)
+- Test: `apps/predbat/tests/test_prediction_batch.py`
+
+**Interfaces:**
+- Consumes: `Prediction.batch_threads` (Task 4), `run_prediction_kernel_batch`'s `n_threads` (Task 2).
+- Produces: nothing new — `batch_threads` is now derived from `args["threads"]`.
+
+`pk_run_batch` was measured bit-identical at every thread count in the C-level test (Task 2 pins that), so any divergence here is a Python bug, not a race in the kernel.
+
+- [ ] **Step 1: Write the failing test**
+
+Add to `apps/predbat/tests/test_prediction_batch.py`:
+
+```python
+def test_batch_threads_do_not_change_results(my_predbat):
+    """A threaded flush must return exactly what a serial one does, returns True on failure.
+
+    pk_run_batch is pinned bit-identical across thread counts at the C level (kernel_parity), so this
+    is really checking the Python side: that results are matched back to their own job regardless of
+    how the kernel split the work.
+    """
+    print("**** Running batch threading tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+    socs = [round(my_predbat.soc_max * fraction, 2) for fraction in (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0)]
+
+    reference = None
+    for n_threads in (1, 2, 8):
+        prediction.batch_threads = n_threads
+        prediction.prediction_cache = {}
+        handles = [prediction.queue_run_prediction_charge(soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record) for soc in socs]
+        results = [handle.get() for handle in handles]
+        if reference is None:
+            reference = results
+        elif results != reference:
+            for index, (got, want) in enumerate(zip(results, reference)):
+                if got != want:
+                    print("ERROR: job {} at {} threads returned {} expected {}".format(index, n_threads, got, want))
+            failed = True
+    prediction.batch_threads = 1
+
+    if not failed:
+        print("Batch threading tests passed - identical results at 1, 2 and 8 threads")
+    return failed
+```
+
+and call it from `run_prediction_batch_tests` alongside the others.
+
+- [ ] **Step 2: Run it and watch it pass already**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all --test prediction_batch > /tmp/predbat-task5.log 2>&1; grep -nE "ERROR|FAIL|passed|Traceback" /tmp/predbat-task5.log | head -20
+```
+
+This one is expected to pass immediately — the plumbing landed in Task 4; the test exists to pin it before the config starts driving it. If it fails, stop: that is a real threading bug and the config must not be wired until it is understood.
+
+- [ ] **Step 3: Wire the config**
+
+In `apps/predbat/plan.py`, re-add the import:
+
+```python
+from multiprocessing import cpu_count
+```
+
+and replace the placeholder from Task 4 with:
+
+```python
+        # The kernel spreads one batched fan-out across threads with the GIL released for the whole
+        # call, so these are real cores - unlike a Python ThreadPool, which peaked at 1.15x on two
+        # threads and then degraded below serial (perf/threadpool-prototype).
+        threads = self.get_arg("threads", "auto")
+        if threads == "auto":
+            self.prediction.batch_threads = cpu_count()
+        else:
+            self.prediction.batch_threads = max(int(threads), 1)
+        self.log("Prediction batch using {} kernel thread(s)".format(self.prediction.batch_threads))
+```
+
+- [ ] **Step 4: Verify plans are byte-identical with threading on**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && python3 ../apps/predbat/unit_test.py --random-run --random-template cases/predbat_debug_agile1.yaml --random-scenarios cases/random_scenarios.yaml > /tmp/predbat-task5-run.log 2>&1; python3 ../apps/predbat/unit_test.py --random-compare cases/random_results.json random_results.json > /tmp/predbat-task5-cmp.log 2>&1; tail -30 /tmp/predbat-task5-cmp.log
+```
+
+Note the benchmark harness may set `threads: 0` (as `annual.py` does deliberately). Check which the run used — grep the run log for `Prediction batch using` — and if it is 1, re-run the benchmark with the template's `threads` raised to `auto` to actually exercise threading before claiming a number.
+
+- [ ] **Step 5: Benchmark, best of 3**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && for i in 1 2 3; do python3 ../apps/predbat/unit_test.py --random-run --random-template cases/predbat_debug_agile1.yaml --random-scenarios cases/random_scenarios.yaml > /tmp/predbat-bench-threaded-$i.log 2>&1; python3 -c "import json;print(round(sum(s['runtime_s'] for s in json.load(open('random_results.json'))['results']), 2))"; done
+```
+
+Record serial vs threaded against the 31.6s baseline. Remember what the brief predicts: the batch call itself is not faster (0.99x vs a loop of `pk_run`) — the win is the Python-side marshalling plus, now, the threads. Kernel time was already down to ~21.6% of plan time, so Amdahl caps what threading alone can return.
+
+- [ ] **Step 6: Update the docs**
+
+`docs/apps-yaml.md`, the `threads` section:
+
+```markdown
+### threads
+
+If defined sets the number of threads to use during plan calculation, the default is 'auto' which will use the same number of threads as you have CPUs in your system.
+
+Predbat batches each group of simulations into a single call to the C++ prediction kernel, which then
+spreads them across this many threads. Results do not depend on the thread count - the same plan is
+produced at any setting - so this only trades CPU for planning time.
+
+Valid values are:
+
+- 'auto' - Use the same number of threads as your CPU count
+```
+
+Keep the rest of the section as it stands, and check whether the list below it still describes reality (e.g. any wording about "0 - no threading" is still correct: 0 means serial).
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && grep -rn "threads" docs/*.md apps/predbat/*.yaml 2>/dev/null | grep -vi "threshold" | head -20
+```
+
+Update anything else that describes threads as worker processes.
+
+- [ ] **Step 7: Full suite and commit**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all > /tmp/predbat-task5-all.log 2>&1; grep -nE "FAILED|ERROR|Traceback|tests passed" /tmp/predbat-task5-all.log | tail -20
+```
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && ./run_pre_commit && git add apps/predbat/plan.py apps/predbat/tests/test_prediction_batch.py docs/apps-yaml.md && git commit -m "perf(plan): run each batched fan-out across kernel threads
+
+args[threads] now sizes the kernel's thread pool rather than a pool of worker processes. The GIL is
+released for the whole pk_run_batch call, so unlike a Python ThreadPool these are real cores.
+
+Identical results at 1, 2 and 8 threads; 20 random scenarios byte-identical.
+
+Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+### Task 6: Delete the process pool
+
+**Files:**
+- Modify: `apps/predbat/plan.py` (`DummyThread`, pool close/join, unused imports)
+- Modify: `apps/predbat/prediction.py` (`wrapped_run_prediction_*`, `PRED_GLOBAL`, `reset_prediction_globals`)
+- Modify: `apps/predbat/predbat.py` (`self.pool` lifecycle)
+- Modify: `apps/predbat/annual.py:1004`, `apps/predbat/marginal.py:89` (comments referencing `PRED_GLOBAL`)
+- Delete: `PHASE_B_BATCH_REFACTOR.md`
+
+**Interfaces:**
+- Consumes: everything from Tasks 1-5.
+- Produces: no `self.pool`, no `PRED_GLOBAL`, no `DummyThread`, no `wrapped_run_prediction_*`.
+
+**Stated consequence:** with the pool gone, a Predbat install where the kernel is unavailable (no binary for the architecture, a stale binary, or `debug_enable` on) plans serially in Python where it used to fan out across processes. The pool was measured at 1.11x on 16 cores with the kernel active; it will be worth more than that without one. This is the brief's call, made explicitly — if that regression is not acceptable, the alternative is to keep the pool solely for the `not kernel_supported()` case, which means keeping `wrapped_run_prediction_*` and `PRED_GLOBAL` alive with it.
+
+- [ ] **Step 1: Find every reference before deleting anything**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && grep -rn "PRED_GLOBAL\|wrapped_run_prediction\|DummyThread\|reset_prediction_globals\|\.pool\b\|apply_async" apps/predbat/ --include="*.py" > /tmp/predbat-pool-refs.txt; cat /tmp/predbat-pool-refs.txt
+```
+
+Work the list top to bottom. Note `apps/predbat/tests/` may reference these too.
+
+- [ ] **Step 2: Delete the dead code**
+
+- `apps/predbat/prediction.py`: remove `PRED_GLOBAL`, `reset_prediction_globals`, all four `wrapped_run_prediction_*`, the `global PRED_GLOBAL` lines in `__init__`, and the `PRED_GLOBAL["dict"] = self.__dict__.copy()` line. `self.pending_batch = []` / `self.batch_threads = 1` stay where they are (the comment above them about the PRED_GLOBAL snapshot no longer applies — delete it).
+- `apps/predbat/plan.py`: remove `DummyThread` and its preamble comment, and the pool `close()`/`join()` blocks at ~1621-1628. Check whether `import time` is still used elsewhere in the file before removing it.
+- `apps/predbat/predbat.py`: remove the `self.pool` attribute and its terminate/close/join blocks (lines ~283-288, 320, 1180-1188, 1612, 1735-1743).
+- `apps/predbat/annual.py`: the `predbat.args["threads"] = 0` line stays (it now means serial batching, which is still what a per-day annual run wants), but rewrite the comment — the PRED_GLOBAL/spawn-safety reasoning it gives is gone. Something like: "The annual tool plans hundreds of small, fast days per run, where splitting each fan-out across kernel threads costs more in thread setup than it returns."
+- `apps/predbat/marginal.py:89`: drop the "this updates PRED_GLOBAL which is safe since we run synchronously" comment; the remaining sentence about not needing 10% extra load stays.
+
+- [ ] **Step 3: Verify nothing references the removed names**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && grep -rn "PRED_GLOBAL\|wrapped_run_prediction\|DummyThread\|reset_prediction_globals\|apply_async\|self\.pool" apps/predbat/ --include="*.py"
+```
+
+Expected: no output.
+
+- [ ] **Step 4: Full suite plus the parity gate**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && python3 ../apps/predbat/unit_test.py --random-run --random-template cases/predbat_debug_agile1.yaml --random-scenarios cases/random_scenarios.yaml > /tmp/predbat-task6-run.log 2>&1; python3 ../apps/predbat/unit_test.py --random-compare cases/random_results.json random_results.json > /tmp/predbat-task6-cmp.log 2>&1; tail -30 /tmp/predbat-task6-cmp.log
+```
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all > /tmp/predbat-task6-all.log 2>&1; grep -nE "FAILED|ERROR|Traceback|tests passed|seconds" /tmp/predbat-task6-all.log | tail -25
+```
+
+`annual_integration` is the one most likely to notice this task — it explicitly sets `threads: 0` and its comment about the pool is what changed. Check its timing in the log against the ~34s the brief records.
+
+- [ ] **Step 5: Delete the brief**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && git rm PHASE_B_BATCH_REFACTOR.md
+```
+
+- [ ] **Step 6: Commit**
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && ./run_pre_commit && git add -A apps/predbat docs && git commit -m "refactor(plan): remove the prediction process pool
+
+Nothing has launched work into it since the fan-out started batching, so PRED_GLOBAL, the four
+wrapped_run_prediction_* trampolines, DummyThread and the pool lifecycle all go with it. The pool was
+measured at 1.11x on 16 cores, with the parent's per-call pickling as the serial bottleneck.
+
+20 random scenarios byte-identical.
+
+Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+## Final verification before the branch is offered for merge
+
+Run all four gates from the brief, in this order, and paste the actual output rather than summarising it:
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && python3 ../apps/predbat/unit_test.py --random-run --random-template cases/predbat_debug_agile1.yaml --random-scenarios cases/random_scenarios.yaml > /tmp/predbat-final-run.log 2>&1; python3 ../apps/predbat/unit_test.py --random-compare cases/random_results.json random_results.json > /tmp/predbat-final-cmp.log 2>&1; tail -30 /tmp/predbat-final-cmp.log
+```
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all --test kernel_parity --test prediction_batch > /tmp/predbat-final-parity.log 2>&1; grep -nE "ERROR|FAILED|passed" /tmp/predbat-final-parity.log | tail -20
+```
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred/coverage && source setup.csh && ./run_all > /tmp/predbat-final-all.log 2>&1; grep -nE "FAILED|ERROR|Traceback|tests passed|seconds" /tmp/predbat-final-all.log | tail -25
+```
+
+```bash
+cd /Users/treforsouthwell/predbat/batpred && ./run_pre_commit
+```
+
+Required: 20 scenarios unchanged with `+0.0000` on metric, cost and all three PV futures; kernel parity green; full suite green including the slow tests; pre-commit clean (re-run and re-stage after the cspell dictionary auto-sorts).
+
+Then report, best of 3 each: serial batch vs threaded batch vs the 31.6s pre-Phase-B baseline.
+
+## Notes for whoever picks this up
+
+- **`PR #4536` (`perf/window-bounds-cache`) is in flight and independent**, worth -21% on its own. Batching partly subsumes it — a batch ships window bounds once per fan-out rather than per call — but the `sim_hash` half still matters, and `prediction_cache_key()` in `prediction_batch.py` is exactly where that half would land. If both branches merge, re-profile before assuming what is left.
+- **`round_py` is poison in any hot loop** — `snprintf`+`strtod`, ~96ns, serialising on libc's global locale, measured 17x degradation at 8 threads. Two calls remain in the kernel (`iboost_next`, `car_soc_next`), both once per run. Do not add a third.
+- **Any new test that calls `calculate_plan` must not use the shared `my_predbat` fixture** — it arrives carrying whatever earlier tests left on it and `calculate_plan` raises. Follow `run_annual_integration_isolated` in `unit_test.py`: take a fresh instance from `create_predbat()` and load a debug YAML.
+- **Do not re-litigate the dead ends**: Python `ThreadPool` (1.15x peak, then below serial), the process pool itself (1.11x on 16 cores), and expecting `pk_run_batch` to beat a loop of `pk_run` on its own (0.99x). All three were measured on this work.

From 76473587bf20036a36be05ff03a8a0b6adfb6988 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 18:57:38 +0100
Subject: [PATCH 07/24] fix(prediction): build the export trial window
 privately instead of mutating the caller's

The trial start was written straight into the caller's window dict, which was only safe because each
pool worker mutated its own unpickled copy. A batched fan-out shares one window list across every job
in the batch, so this has to be local. Ported from perf/threadpool-prototype.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/prediction.py                  | 23 ++++--
 apps/predbat/tests/test_prediction_batch.py | 82 +++++++++++++++++++++
 apps/predbat/unit_test.py                   |  2 +
 3 files changed, 102 insertions(+), 5 deletions(-)
 create mode 100644 apps/predbat/tests/test_prediction_batch.py

diff --git a/apps/predbat/prediction.py b/apps/predbat/prediction.py
index 2cc7053a2..d018eb8b1 100644
--- a/apps/predbat/prediction.py
+++ b/apps/predbat/prediction.py
@@ -355,11 +355,15 @@ def thread_run_prediction_charge_min_max(self, try_soc, window_n, charge_limit,
             max_soc,
         )
 
-    def thread_run_prediction_export(self, this_export_limit, start, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """
-        Run prediction in a thread
+    def _prepare_export(self, this_export_limit, start, window_n, export_window, export_limits, all_n):
+        """Build the trial export limits and window list - shared by thread_run_prediction_export and the batch path.
+
+        The trial start is applied to a private copy of the window rather than written into the
+        caller's list: with a process pool each worker mutated its own unpickled copy, but a batched
+        fan-out shares one list across every job in the batch, so an in-place write would corrupt the
+        other trials of the same window. Only ["end"] is ever read back by the caller
+        (optimise_export), so nothing depends on the write being visible.
         """
-        # Store try value into the window
         export_limits = export_limits.copy()
 
         if all_n:
@@ -370,7 +374,16 @@ def thread_run_prediction_export(self, this_export_limit, start, window_n, charg
             # Adjust start
             window = export_window[window_n]
             start = min(start, window["end"] - 5)
-            export_window[window_n]["start"] = start
+            export_window = list(export_window)
+            export_window[window_n] = dict(window, start=start)
+
+        return export_window, export_limits
+
+    def thread_run_prediction_export(self, this_export_limit, start, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
+        """
+        Run prediction in a thread
+        """
+        export_window, export_limits = self._prepare_export(this_export_limit, start, window_n, export_window, export_limits, all_n)
 
         (
             metricmid,
diff --git a/apps/predbat/tests/test_prediction_batch.py b/apps/predbat/tests/test_prediction_batch.py
new file mode 100644
index 000000000..059317aac
--- /dev/null
+++ b/apps/predbat/tests/test_prediction_batch.py
@@ -0,0 +1,82 @@
+# -----------------------------------------------------------------------------
+# Predbat Home Battery System
+# Copyright Trefor Southwell 2026 - All Rights Reserved
+# This application maybe used for personal use only and not for commercial use
+# -----------------------------------------------------------------------------
+# fmt off
+# pylint: disable=consider-using-f-string
+# pylint: disable=line-too-long
+
+"""Tests for the batched prediction fan-out.
+
+launch_run_prediction_* no longer runs anything: it queues a job on the Prediction and returns a
+handle whose first get() flushes the whole batch through one pk_run_batch call. These tests pin the
+two things that makes conditional - that a queued job returns exactly what the direct
+thread_run_prediction_* path returns, and that a job the kernel cannot take still runs.
+"""
+
+
+from prediction import Prediction
+
+
+def make_export_windows(minutes_now):
+    """Build a small deterministic export window layout for the trial-input tests"""
+    return [
+        {"start": minutes_now + 60, "end": minutes_now + 120, "average": 15.0},
+        {"start": minutes_now + 180, "end": minutes_now + 240, "average": 20.0},
+    ]
+
+
+def test_export_trial_does_not_mutate_caller_window(my_predbat):
+    """The export trial must build its own window list, returns True on failure.
+
+    It used to write the trial start straight into the caller's window dict, which was only safe
+    because each pool worker mutated its own unpickled copy. A batched fan-out holds one shared list
+    across every job in the batch, so an in-place write would corrupt every other trial of the same
+    window.
+    """
+    print("**** Running export trial input tests ****")
+    failed = False
+    minutes_now = my_predbat.minutes_now
+    export_window = make_export_windows(minutes_now)
+    export_limits = [100.0, 100.0]
+    original = [dict(window) for window in export_window]
+
+    prediction = Prediction(my_predbat, {}, {}, {}, {})
+    trial_window, trial_limits = prediction._prepare_export(5.0, minutes_now + 90, 0, export_window, export_limits, None)
+
+    if export_window != original:
+        print("ERROR: _prepare_export mutated the caller's export window: {} vs {}".format(export_window, original))
+        failed = True
+    if export_limits != [100.0, 100.0]:
+        print("ERROR: _prepare_export mutated the caller's export limits: {}".format(export_limits))
+        failed = True
+    if trial_window[0]["start"] != minutes_now + 90:
+        print("ERROR: trial window start not applied, got {}".format(trial_window[0]["start"]))
+        failed = True
+    if trial_window[1] is not export_window[1]:
+        print("ERROR: untouched windows should be shared with the caller's list, not copied")
+        failed = True
+    if trial_limits[0] != 5.0:
+        print("ERROR: trial export limit not applied, got {}".format(trial_limits[0]))
+        failed = True
+
+    # The trial start is clamped to at least 5 minutes before the window end
+    trial_window, _ = prediction._prepare_export(5.0, minutes_now + 200, 0, export_window, export_limits, None)
+    if trial_window[0]["start"] != minutes_now + 115:
+        print("ERROR: trial window start not clamped to end-5, got {}".format(trial_window[0]["start"]))
+        failed = True
+
+    if not failed:
+        print("Export trial input tests passed")
+    return failed
+
+
+def run_prediction_batch_tests(my_predbat):
+    """Run every batched prediction test, returns True on failure"""
+    failed = test_export_trial_does_not_mutate_caller_window(my_predbat)
+    if failed:
+        print("**** Prediction batch tests FAILED ****")
+    else:
+        print("**** Prediction batch tests passed ****")
+    return failed
diff --git a/apps/predbat/unit_test.py b/apps/predbat/unit_test.py
index 16fac4fd8..03973c7f5 100644
--- a/apps/predbat/unit_test.py
+++ b/apps/predbat/unit_test.py
@@ -23,6 +23,7 @@
 from tests.test_model import run_model_tests
 from tests.test_predict_pv_power import run_predict_pv_power_tests
 from tests.test_kernel_parity import run_kernel_parity_tests, run_model_kernel_tests
+from tests.test_prediction_batch import run_prediction_batch_tests
 from tests.test_execute import run_execute_tests
 from tests.test_load_car_energy import test_load_car_energy_warns_when_configured_entity_has_no_data
 from tests.test_octopus_slots import run_load_octopus_slots_tests
@@ -297,6 +298,7 @@ def main():
         ("predict_pv_power", run_predict_pv_power_tests, "predict_pv_power plan-interval scaling tests", False),
         ("model_kernel", run_model_kernel_tests, "Model tests run with the C++ prediction kernel enabled", False),
         ("kernel_parity", run_kernel_parity_tests, "C++ prediction kernel vs Python engine parity tests", False),
+        ("prediction_batch", run_prediction_batch_tests, "Batched prediction fan-out tests", False),
         ("inverter", run_inverter_tests, "Inverter tests", False),
         ("execute", run_execute_tests, "Execute tests", False),
         ("load_car_energy", test_load_car_energy_warns_when_configured_entity_has_no_data, "car_charging_energy configured-but-empty warning tests (#4458 follow-up)", False),

From 877cc8b9fde36bd00bdc492bf97737e7d895b495 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 19:03:52 +0100
Subject: [PATCH 08/24] chore: green the quality gate on the batching branch

The hand-off notes landed without passing cspell or markdownlint, so pre-commit has been red on this
branch since. Adds the five technical words the notes use and tags their two code fences.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .cspell/custom-dictionary-workspace.txt | 5 +++++
 PHASE_B_BATCH_REFACTOR.md               | 7 +++++--
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/.cspell/custom-dictionary-workspace.txt b/.cspell/custom-dictionary-workspace.txt
index ee6ebdf9c..6ef384d4b 100644
--- a/.cspell/custom-dictionary-workspace.txt
+++ b/.cspell/custom-dictionary-workspace.txt
@@ -51,6 +51,7 @@ byok
 cadata
 calib
 Cantarell
+CDLL
 cdn
 Cerbo
 cexxxx
@@ -77,6 +78,7 @@ corruptplans
 cprofile
 creds
 crosscharge
+ctypes
 customauthorizer
 customisation
 Customise
@@ -491,6 +493,7 @@ temperation
 Teslemetry
 testname
 thirdparty
+threadpool
 timea
 timeb
 timec
@@ -522,6 +525,8 @@ unconfigured
 undiscounted
 unnormalised
 unparseable
+unpickled
+unpushed
 unsmoothed
 unstaged
 urlsafe
diff --git a/PHASE_B_BATCH_REFACTOR.md b/PHASE_B_BATCH_REFACTOR.md
index 35dd7dfb6..7d98291cf 100644
--- a/PHASE_B_BATCH_REFACTOR.md
+++ b/PHASE_B_BATCH_REFACTOR.md
@@ -23,7 +23,7 @@ three PV futures, `kernel_parity` green, full suite green including slow tests (
 
 Measured on `cases/random_scenarios.yaml` scenario 0 (the benchmark scenario), `threads=0`:
 
-```
+```text
 calculate_plan   2418.4 ms  ->  1758.5 ms     -27%
   C++ pk_run     1056.5 ms  ->   380.5 ms
   Python         1361.9 ms  ->  1378.0 ms     unchanged, as expected
@@ -71,7 +71,7 @@ to change at all** — only what `launch_*` returns and when the work actually h
 
 Batch sizes this produces (scenario 0, from the profile):
 
-```
+```text
 launch_run_prediction_charge          10,698
 launch_run_prediction_export           5,436
 launch_run_prediction_single           4,752
@@ -79,6 +79,7 @@ launch_run_prediction_charge_min_max   3,294
                                       ------
                                       24,180 launches, ~19,209 reaching the kernel
 ```
+
 `optimise_charge_limit_price_threads` fans out ~1176 at a time (392 candidates x 3 PV scenarios),
 `optimise_charge_limit` ~900, `optimise_export` ~628. Those are the batches.
 
@@ -140,10 +141,12 @@ A per-job `status` is returned precisely so one malformed scenario cannot discar
 1. **`thread_run_prediction_export` used to mutate the caller's window dict** (`export_window[n]["start"] = start`).
    That was only safe because each pool worker mutated its own unpickled copy. Any shared-memory
    parallelism needs it local. The fix is on branch `perf/threadpool-prototype`:
+
    ```python
    export_window = list(export_window)
    export_window[window_n] = dict(window, start=start)
    ```
+
    Only `["end"]` is ever read back by `optimise_export`, so nothing depends on the write being
    visible. **Port this across.**
 

From 30d025e203a262bd021e7f6b05f857d13ce8297b Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 19:12:08 +0100
Subject: [PATCH 09/24] feat(kernel): marshal a whole fan-out into one
 pk_run_batch call

Adds BatchJob, run_prediction_kernel_batch and the shared result assembly, and gives pk_run_batch's
ctypes binding the n_threads argument it was missing. Window start/end arrays are marshalled once per
distinct window list rather than once per job, which is where the batching win comes from.

Also adds Prediction.scan_soc_range and routes thread_run_prediction_charge_min_max through it so the
scan logic is not duplicated ahead of the batch runner landing in a later task.

Bit-identical to a loop of pk_run across 60 scenarios at 1, 2, 4 and 8 threads.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/prediction.py               |  29 +++-
 apps/predbat/prediction_kernel.py        | 201 ++++++++++++++++++-----
 apps/predbat/tests/test_kernel_parity.py | 113 +++++++++++++
 3 files changed, 296 insertions(+), 47 deletions(-)

diff --git a/apps/predbat/prediction.py b/apps/predbat/prediction.py
index d018eb8b1..91c7bfb83 100644
--- a/apps/predbat/prediction.py
+++ b/apps/predbat/prediction.py
@@ -295,6 +295,25 @@ def thread_run_prediction_charge(self, try_soc, window_n, charge_limit, charge_w
             final_carbon_g,
         )
 
+    def scan_soc_range(self, predict_soc, window):
+        """Return the (min, max) SoC across a charge window - shared by the direct and batch min/max paths.
+
+        The kernel computes the same range inline (see PkBatchJob.soc_range_start_step), so this is
+        only reached when a job falls back to the Python engine; the two must agree exactly, including
+        the clamping that collapses an empty range to a single value rather than leaving min above max.
+        """
+        min_soc = self.soc_max
+        max_soc = 0
+        predict_minute_start = max(int((window["start"] - self.minutes_now) / 5) * 5, 0)
+        predict_minute_end = int((window["end"] - self.minutes_now) / 5) * 5
+        for minute in range(predict_minute_start, predict_minute_end + 5, 5):
+            if minute in predict_soc:
+                min_soc = min(predict_soc[minute], min_soc)
+                max_soc = max(predict_soc[minute], max_soc)
+        max_soc = max(max_soc, min_soc)
+        min_soc = min(min_soc, max_soc)
+        return min_soc, max_soc
+
     def thread_run_prediction_charge_min_max(self, try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
         """
         Run prediction in a thread
@@ -329,15 +348,7 @@ def thread_run_prediction_charge_min_max(self, try_soc, window_n, charge_limit,
         min_soc = self.soc_max
         max_soc = 0
         if not all_n:
-            window = charge_window[window_n]
-            predict_minute_start = max(int((window["start"] - self.minutes_now) / 5) * 5, 0)
-            predict_minute_end = int((window["end"] - self.minutes_now) / 5) * 5
-            for minute in range(predict_minute_start, predict_minute_end + 5, 5):
-                if minute in predict_soc:
-                    min_soc = min(predict_soc[minute], min_soc)
-                    max_soc = max(predict_soc[minute], max_soc)
-            max_soc = max(max_soc, min_soc)
-            min_soc = min(min_soc, max_soc)
+            min_soc, max_soc = self.scan_soc_range(predict_soc, charge_window[window_n])
 
         return (
             cost,
diff --git a/apps/predbat/prediction_kernel.py b/apps/predbat/prediction_kernel.py
index 7d90317cd..d90005a61 100644
--- a/apps/predbat/prediction_kernel.py
+++ b/apps/predbat/prediction_kernel.py
@@ -212,6 +212,33 @@ class PkBatchResult(ctypes.Structure):
     ]
 
 
+class BatchJob:
+    """One queued prediction: its trial inputs, how the result should be shaped, and the result itself.
+
+    run_prediction_kernel_batch reads only the input fields and the SoC range steps. sim_hash,
+    want_range, range_window and result belong to the batch runner in prediction_batch.py, which is
+    the only thing that creates these; they live here so the ctypes layer can be tested on its own.
+    """
+
+    __slots__ = ("charge_limit", "charge_window", "export_window", "export_limits", "pv_scenario", "end_record", "step", "soc_range_start_step", "soc_range_end_step", "sim_hash", "want_range", "range_window", "result")
+
+    def __init__(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step, soc_range_start_step=-1, soc_range_end_step=-1, sim_hash=None, want_range=False, range_window=None):
+        """Record one job's trial inputs; result stays None until the batch is flushed"""
+        self.charge_limit = charge_limit
+        self.charge_window = charge_window
+        self.export_window = export_window
+        self.export_limits = export_limits
+        self.pv_scenario = pv_scenario
+        self.end_record = end_record
+        self.step = step
+        self.soc_range_start_step = soc_range_start_step
+        self.soc_range_end_step = soc_range_end_step
+        self.sim_hash = sim_hash
+        self.want_range = want_range
+        self.range_window = range_window
+        self.result = None
+
+
 def kernel_library_candidates():
     """Return the ordered list of shared library paths to try loading.
 
@@ -286,7 +313,9 @@ def load_kernel(log=None):
             lib.pk_run.argtypes = [ctypes.c_int64, ctypes.POINTER(PkScenario), ctypes.POINTER(PkResult)]
             try:
                 lib.pk_run_batch.restype = ctypes.c_int32
-                lib.pk_run_batch.argtypes = [ctypes.c_int64, ctypes.POINTER(PkBatchJob), ctypes.c_int32, ctypes.POINTER(PkBatchResult)]
+                # The trailing c_int32 is n_threads - pk_run_batch takes five arguments, and ctypes
+                # will happily pass a fourth-and-a-bit if the binding says otherwise
+                lib.pk_run_batch.argtypes = [ctypes.c_int64, ctypes.POINTER(PkBatchJob), ctypes.c_int32, ctypes.POINTER(PkBatchResult), ctypes.c_int32]
                 KERNEL_HAS_BATCH = True
             except AttributeError:
                 KERNEL_HAS_BATCH = False
@@ -535,6 +564,136 @@ def kernel_supported(pred, save, step):
     return not save and not pred.debug_enable and getattr(pred, "kernel_handle", 0) != 0
 
 
+def kernel_result_tuple(pred, result, predict_soc):
+    """Assemble the 17-field Prediction.run_prediction() result tuple from a PkResult.
+
+    Shared by the single-scenario and batch paths - mirrors prediction.py:626-628, 1266-1284.
+    """
+    car_charging_soc_next = pred.car_charging_soc_next[:]
+    if result.car_soc_next_valid:
+        for car_n in range(pred.num_cars):
+            car_charging_soc_next[car_n] = result.car_soc_next[car_n]
+
+    iboost_next = result.iboost_next if pred.iboost_enable else pred.iboost_next
+
+    return (
+        round(result.final_metric, 4),
+        round(result.import_kwh_battery, 4),
+        round(result.import_kwh_house, 4),
+        round(result.export_kwh, 4),
+        round(result.soc_min, 4),
+        round(result.final_soc, 4),
+        result.soc_min_minute,
+        round(result.battery_cycle, 4),
+        round(result.metric_keep, 4),
+        round(result.final_iboost, 4),
+        round(result.final_carbon_g, 4),
+        predict_soc,
+        car_charging_soc_next,
+        iboost_next,
+        bool(result.iboost_running),
+        bool(result.iboost_running_solar),
+        bool(result.iboost_running_full),
+    )
+
+
+def reset_kernel_run_state(pred):
+    """Clear the per-run state attributes exactly as the Python engine does - prediction.py:414-422
+
+    Non-save runs never populate these, so they simply stay empty/False; a kernel run has to leave
+    the Prediction in the same state a Python run would.
+    """
+    pred.predict_soc_best = {}
+    pred.predict_metric_best = {}
+    pred.predict_iboost_best = {}
+    pred.predict_carbon_best = {}
+    pred.predict_clipped_best = {}
+    pred.iboost_running = False
+    pred.iboost_running_solar = False
+    pred.iboost_running_full = False
+
+
+def run_prediction_kernel_batch(pred, jobs, n_threads=1):
+    """Run a whole fan-out of prediction scenarios through the kernel in one pk_run_batch call.
+
+    Returns a list of (result_tuple, soc_range_min, soc_range_max) in job order, where result_tuple
+    is the same 17-field tuple Prediction.run_prediction() returns, or None for a job the kernel
+    refused - the caller re-runs those through the Python engine. Returns None when the batch could
+    not be run at all (no kernel, no batch entry point, or a non-zero return code).
+
+    Whether the kernel is allowed to answer at all is the caller's decision, not this function's:
+    kernel_supported() also refuses debug and saving runs, and flush_batch checks it before getting
+    here.
+
+    Batched jobs never materialise the per-minute SoC series: their callers all discard it, and one
+    buffer per job would be ~84MB on a large batch. The one thing a caller does want from it - the
+    SoC range across a charge window - the kernel tracks inline over
+    [soc_range_start_step, soc_range_end_step] instead.
+    """
+    lib = KERNEL_LIB
+    if not lib or not KERNEL_HAS_BATCH or not getattr(pred, "kernel_handle", 0):
+        return None
+
+    n_jobs = len(jobs)
+    job_array = (PkBatchJob * n_jobs)()
+    result_array = (PkBatchResult * n_jobs)()
+    # Every ctypes buffer behind a pointer field has to outlive the call. Structures reached through
+    # an array index do not keep their own _objects, so the buffers are held here rather than relying
+    # on ctypes' keepalive doing the right thing through the array.
+    buffers = []
+    # A fan-out reuses the same window lists across most of its jobs, so their start/end arrays are
+    # marshalled once per distinct list instead of once per job - that is the bulk of the batching
+    # win. Keyed on identity, with the list retained so an id() cannot be recycled mid-batch. This
+    # relies on no caller mutating a window list between enqueue and flush (see prediction_batch.py).
+    window_cache = {}
+
+    def window_arrays(windows):
+        """Marshal a window list's start/end arrays, reusing an earlier job's arrays where possible"""
+        entry = window_cache.get(id(windows))
+        if entry is None:
+            entry = (int32_array([window["start"] for window in windows]), int32_array([window["end"] for window in windows]), windows)
+            window_cache[id(windows)] = entry
+        return entry
+
+    for index, job in enumerate(jobs):
+        charge_start, charge_end, _ = window_arrays(job.charge_window)
+        export_start, export_end, _ = window_arrays(job.export_window)
+        charge_limit = double_array(job.charge_limit)
+        export_limits = double_array(job.export_limits)
+        buffers.append((charge_limit, export_limits))
+
+        pk_job = job_array[index]
+        pk_job.charge_limit = charge_limit
+        pk_job.charge_start = charge_start
+        pk_job.charge_end = charge_end
+        pk_job.export_limits = export_limits
+        pk_job.export_start = export_start
+        pk_job.export_end = export_end
+        pk_job.soc_out = None
+        pk_job.n_charge = len(job.charge_window)
+        pk_job.n_export = len(job.export_window)
+        pk_job.pv_scenario = int(job.pv_scenario)
+        pk_job.end_record = job.end_record
+        pk_job.step = PREDICT_STEP  # the caller's step is ignored - see run_prediction_kernel
+        pk_job.soc_range_start_step = job.soc_range_start_step
+        pk_job.soc_range_end_step = job.soc_range_end_step
+
+    return_code = lib.pk_run_batch(pred.kernel_handle, job_array, n_jobs, result_array, max(int(n_threads), 1))
+    if return_code != 0:
+        return None
+
+    reset_kernel_run_state(pred)
+
+    results = []
+    for index in range(n_jobs):
+        batch_result = result_array[index]
+        if batch_result.status != 0:
+            results.append((None, 0.0, 0.0))
+            continue
+        results.append((kernel_result_tuple(pred, batch_result.result, {}), batch_result.soc_range_min, batch_result.soc_range_max))
+    return results
+
+
 def run_prediction_kernel(pred, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step, cache):
     """Run one prediction scenario through the C++ kernel.
 
@@ -582,18 +741,9 @@ def run_prediction_kernel(pred, charge_limit, charge_window, export_window, expo
     if return_code != 0:
         return None
 
-    # Reset the per-run state attributes exactly as the Python engine does - prediction.py:414-422
-    # (non-save runs never populate these, so they remain empty/False)
-    pred.predict_soc_best = {}
-    pred.predict_metric_best = {}
-    pred.predict_iboost_best = {}
-    pred.predict_carbon_best = {}
-    pred.predict_clipped_best = {}
-    pred.iboost_running = False
-    pred.iboost_running_solar = False
-    pred.iboost_running_full = False
+    reset_kernel_run_state(pred)
 
-    # Assemble the same return value as the Python engine - prediction.py:626-628, 1266-1284
+    # Assemble the same return value as the Python engine
     predict_soc = {}
     if not cache:
         # Indexed loop, not dict(zip(range(...), soc_out)): iterating a ctypes array boxes each double
@@ -601,29 +751,4 @@ def run_prediction_kernel(pred, charge_limit, charge_window, export_window, expo
         for k in range(n_steps):
             predict_soc[k * PREDICT_STEP] = soc_out[k]
 
-    car_charging_soc_next = pred.car_charging_soc_next[:]
-    if result.car_soc_next_valid:
-        for car_n in range(pred.num_cars):
-            car_charging_soc_next[car_n] = result.car_soc_next[car_n]
-
-    iboost_next = result.iboost_next if pred.iboost_enable else pred.iboost_next
-
-    return (
-        round(result.final_metric, 4),
-        round(result.import_kwh_battery, 4),
-        round(result.import_kwh_house, 4),
-        round(result.export_kwh, 4),
-        round(result.soc_min, 4),
-        round(result.final_soc, 4),
-        result.soc_min_minute,
-        round(result.battery_cycle, 4),
-        round(result.metric_keep, 4),
-        round(result.final_iboost, 4),
-        round(result.final_carbon_g, 4),
-        predict_soc,
-        car_charging_soc_next,
-        iboost_next,
-        bool(result.iboost_running),
-        bool(result.iboost_running_solar),
-        bool(result.iboost_running_full),
-    )
+    return kernel_result_tuple(pred, result, predict_soc)
diff --git a/apps/predbat/tests/test_kernel_parity.py b/apps/predbat/tests/test_kernel_parity.py
index 38bd9dbda..666191862 100644
--- a/apps/predbat/tests/test_kernel_parity.py
+++ b/apps/predbat/tests/test_kernel_parity.py
@@ -849,6 +849,117 @@ def run_clipping_parity_tests(my_predbat, count=250):
     return failed
 
 
+def build_batch_jobs(my_predbat, rng, count):
+    """Build a list of BatchJob scenarios plus the Prediction they run against"""
+    apply_random_scenario(my_predbat, rng)
+    pv_step, pv10_step, load_step, load10_step = make_step_data(my_predbat, rng)
+    my_predbat.prediction_kernel_enable = True
+    prediction = Prediction(my_predbat, pv_step, pv10_step, load_step, load10_step)
+    prediction.kernel_handle = create_kernel_context(prediction)
+
+    charge_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, rng.randint(2, 5))
+    export_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, rng.randint(2, 5))
+    end_record = my_predbat.forecast_minutes
+
+    jobs = []
+    for index in range(count):
+        charge_limit = [round(rng.uniform(0, my_predbat.soc_max), 2) for _ in charge_window]
+        export_limits = [rng.choice([100.0, 99.0, 0.0, round(rng.uniform(1, 99), 1)]) for _ in export_window]
+        pv_scenario = rng.choice([PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10, PV_SCENARIO_PV90])
+        # Every third job asks for the SoC range over a charge window, as the min/max fan-out does
+        range_window = charge_window[index % len(charge_window)] if (index % 3) == 0 else None
+        start_step = -1
+        end_step = -1
+        if range_window is not None:
+            start_step = max(int((range_window["start"] - my_predbat.minutes_now) / 5) * 5, 0) // 5
+            end_step = int((range_window["end"] - my_predbat.minutes_now) / 5) * 5 // 5
+        jobs.append(
+            prediction_kernel.BatchJob(
+                charge_limit,
+                charge_window,
+                export_window,
+                export_limits,
+                pv_scenario,
+                end_record,
+                5,
+                soc_range_start_step=start_step,
+                soc_range_end_step=end_step,
+                want_range=range_window is not None,
+                range_window=range_window,
+            )
+        )
+    return prediction, jobs
+
+
+def run_batch_parity_tests(my_predbat, count=60):
+    """Check pk_run_batch matches a loop of pk_run exactly, at every thread count, returns True on failure.
+
+    The batch path is the only one Python will use after this refactor, and it is the only one that
+    can run scenarios concurrently, so it is checked against the single-scenario path it replaces
+    rather than against the Python engine (which run_random_sweep_tests already pins pk_run against).
+    Results must be bit-identical, not merely close: the whole point of the fan-out is that plans do
+    not change.
+    """
+    print("**** Running kernel batch parity tests ****")
+    if not prediction_kernel.KERNEL_HAS_BATCH:
+        print("SKIP: kernel does not expose pk_run_batch")
+        return False
+
+    failed = False
+    prediction, jobs = build_batch_jobs(my_predbat, random.Random(4321), count)
+    if not prediction.kernel_handle:
+        print("ERROR: batch parity kernel context creation failed")
+        return True
+
+    # Reference: one pk_run per scenario, with the SoC series materialised so the range can be
+    # scanned in Python exactly as thread_run_prediction_charge_min_max does
+    reference = []
+    for job in jobs:
+        single = run_prediction_kernel(prediction, job.charge_limit, job.charge_window, job.export_window, job.export_limits, job.pv_scenario, job.end_record, 5, False)
+        if single is None:
+            print("ERROR: reference pk_run failed")
+            return True
+        if job.range_window is None:
+            soc_range = (prediction.soc_max, 0)
+        else:
+            soc_range = prediction.scan_soc_range(single[11], job.range_window)
+        reference.append((single, soc_range))
+
+    for n_threads in (1, 2, 4, 8):
+        batched = prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads)
+        if batched is None:
+            print("ERROR: pk_run_batch failed at {} threads".format(n_threads))
+            return True
+        if len(batched) != len(jobs):
+            print("ERROR: pk_run_batch returned {} results for {} jobs".format(len(batched), len(jobs)))
+            return True
+        for index, (result_tuple, soc_range_min, soc_range_max) in enumerate(batched):
+            single, (expect_min, expect_max) = reference[index]
+            if result_tuple is None:
+                print("ERROR: job {} reported a non-zero status at {} threads".format(index, n_threads))
+                failed = True
+                continue
+            for field, name in enumerate(RESULT_NAMES):
+                if result_tuple[field] != single[field]:
+                    print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
+                    failed = True
+            # The batch never materialises the SoC series - that is what makes it affordable
+            if result_tuple[11] != {}:
+                print("ERROR: job {} returned a SoC series from the batch path".format(index))
+                failed = True
+            for field, name in [(12, "car_charging_soc_next"), (13, "iboost_next"), (14, "iboost_running"), (15, "iboost_running_solar"), (16, "iboost_running_full")]:
+                if result_tuple[field] != single[field]:
+                    print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
+                    failed = True
+            if soc_range_min != expect_min or soc_range_max != expect_max:
+                print("ERROR: job {} at {} threads SoC range {} {} expected {} {}".format(index, n_threads, soc_range_min, soc_range_max, expect_min, expect_max))
+                failed = True
+
+    if not failed:
+        print("Batch parity: {} scenarios bit-identical to pk_run at 1, 2, 4 and 8 threads".format(count))
+    return failed
+
+
 def kernel_available():
     """Ensure the kernel library is built and loaded, returns (available, required_failure)"""
     if not ensure_kernel_built():
@@ -927,6 +1038,8 @@ def run_kernel_parity_tests(my_predbat):
             failed |= run_random_sweep_tests(my_predbat)
         if not failed:
             failed |= run_clipping_parity_tests(my_predbat)
+        if not failed:
+            failed |= run_batch_parity_tests(my_predbat)
     finally:
         restore_scenario_state(my_predbat, state)
 

From 5044b5828ec575504f865254fd17572c014f52a1 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 19:26:57 +0100
Subject: [PATCH 10/24] test(kernel): pin the pk_run_batch argtypes and the
 window memo's cache key

Closes two review findings on the batch entry point: the argtypes fix had no regression test (ctypes
silently accepts extra call arguments, and a garbage n_threads only changes work splitting, not
results, so the bug was invisible to the batch parity sweep), and the window-array memo was only ever
exercised with one shared list per side, never with genuinely distinct lists in the same batch.

Adds a direct argtypes assertion to run_marshalling_tests, and a new run_batch_window_variant_tests
that cycles jobs across several distinct charge/export window lists and checks the batch against the
same per-scenario pk_run reference. Extracted build_batch_reference and check_batch_results out of
run_batch_parity_tests so the new test reuses them instead of duplicating the comparison, which also
brings run_batch_parity_tests back under the repo's flake8 complexity limit.

Also rewords a comment on the buffers keepalive list that overstated why it's needed (ctypes' own
container keepalive already covers it; the list is belt-and-braces, not load-bearing) - comment only,
no code change.

Both new assertions verified to actually catch their target bugs via temporary mutation, reverted
before committing.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/prediction_kernel.py        |   6 +-
 apps/predbat/tests/test_kernel_parity.py | 170 +++++++++++++++++------
 2 files changed, 131 insertions(+), 45 deletions(-)

diff --git a/apps/predbat/prediction_kernel.py b/apps/predbat/prediction_kernel.py
index d90005a61..5ecc7755e 100644
--- a/apps/predbat/prediction_kernel.py
+++ b/apps/predbat/prediction_kernel.py
@@ -637,9 +637,9 @@ def run_prediction_kernel_batch(pred, jobs, n_threads=1):
     n_jobs = len(jobs)
     job_array = (PkBatchJob * n_jobs)()
     result_array = (PkBatchResult * n_jobs)()
-    # Every ctypes buffer behind a pointer field has to outlive the call. Structures reached through
-    # an array index do not keep their own _objects, so the buffers are held here rather than relying
-    # on ctypes' keepalive doing the right thing through the array.
+    # Every ctypes buffer behind a pointer field has to outlive the call. ctypes' own keepalive already
+    # covers this (b_base walks up to job_array._objects), so this list is belt-and-braces: the limit
+    # arrays are held explicitly here rather than relying on ctypes' container keepalive.
     buffers = []
     # A fan-out reuses the same window lists across most of its jobs, so their start/end arrays are
     # marshalled once per distinct list instead of once per job - that is the bulk of the batching
diff --git a/apps/predbat/tests/test_kernel_parity.py b/apps/predbat/tests/test_kernel_parity.py
index 666191862..919b31fd0 100644
--- a/apps/predbat/tests/test_kernel_parity.py
+++ b/apps/predbat/tests/test_kernel_parity.py
@@ -423,6 +423,19 @@ def run_marshalling_tests():
             print("ERROR: {}([]) should be empty, got length {}".format(name, len(empty)))
             failed = True
 
+    # pk_run_batch's binding used to be missing its trailing n_threads entry in argtypes. That bug is invisible
+    # to the batch parity test: ctypes silently accepts more call arguments than argtypes declares, and
+    # a garbage thread count only changes how pk_run_batch splits work across threads - the results are
+    # thread-count-independent by design. Only asserting the binding itself catches a regression here.
+    if prediction_kernel.KERNEL_HAS_BATCH:
+        expected_argtypes = (ctypes.c_int64, ctypes.POINTER(prediction_kernel.PkBatchJob), ctypes.c_int32, ctypes.POINTER(prediction_kernel.PkBatchResult), ctypes.c_int32)
+        actual_argtypes = tuple(prediction_kernel.KERNEL_LIB.pk_run_batch.argtypes)
+        if actual_argtypes != expected_argtypes:
+            print("ERROR: pk_run_batch.argtypes is {} but the C++ signature (handle, jobs, n_jobs, results, n_threads) needs {}".format(actual_argtypes, expected_argtypes))
+            failed = True
+    else:
+        print("SKIP: pk_run_batch argtypes check - kernel does not expose pk_run_batch")
+
     if not failed:
         print("PASS")
     return failed
@@ -849,20 +862,29 @@ def run_clipping_parity_tests(my_predbat, count=250):
     return failed
 
 
-def build_batch_jobs(my_predbat, rng, count):
-    """Build a list of BatchJob scenarios plus the Prediction they run against"""
+def build_batch_jobs(my_predbat, rng, count, window_variants=1):
+    """Build a list of BatchJob scenarios plus the Prediction they run against.
+
+    window_variants controls how many distinct charge/export window lists the batch draws from, cycled
+    by job index (job N uses variant N % window_variants). The default of 1 shares a single list across
+    every job, as the real min/max fan-out does when it scans one window at a time. A caller wanting
+    per-job window lists - to exercise run_prediction_kernel_batch's identity-keyed window memo, whose
+    cache key is id(windows), not its content - passes a larger value.
+    """
     apply_random_scenario(my_predbat, rng)
     pv_step, pv10_step, load_step, load10_step = make_step_data(my_predbat, rng)
     my_predbat.prediction_kernel_enable = True
     prediction = Prediction(my_predbat, pv_step, pv10_step, load_step, load10_step)
     prediction.kernel_handle = create_kernel_context(prediction)
 
-    charge_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, rng.randint(2, 5))
-    export_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, rng.randint(2, 5))
+    charge_windows = [make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, rng.randint(2, 5)) for _ in range(window_variants)]
+    export_windows = [make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, rng.randint(2, 5)) for _ in range(window_variants)]
     end_record = my_predbat.forecast_minutes
 
     jobs = []
     for index in range(count):
+        charge_window = charge_windows[index % window_variants]
+        export_window = export_windows[index % window_variants]
         charge_limit = [round(rng.uniform(0, my_predbat.soc_max), 2) for _ in charge_window]
         export_limits = [rng.choice([100.0, 99.0, 0.0, round(rng.uniform(1, 99), 1)]) for _ in export_window]
         pv_scenario = rng.choice([PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10, PV_SCENARIO_PV90])
@@ -891,6 +913,57 @@ def build_batch_jobs(my_predbat, rng, count):
     return prediction, jobs
 
 
+def build_batch_reference(prediction, jobs):
+    """Run one pk_run per job to build the reference results run_prediction_kernel_batch is checked against.
+
+    The SoC series is materialised (unlike the batch path) so the range can be scanned in Python
+    exactly as thread_run_prediction_charge_min_max does. Returns None if a reference run itself fails.
+    """
+    reference = []
+    for job in jobs:
+        single = run_prediction_kernel(prediction, job.charge_limit, job.charge_window, job.export_window, job.export_limits, job.pv_scenario, job.end_record, 5, False)
+        if single is None:
+            print("ERROR: reference pk_run failed")
+            return None
+        if job.range_window is None:
+            soc_range = (prediction.soc_max, 0)
+        else:
+            soc_range = prediction.scan_soc_range(single[11], job.range_window)
+        reference.append((single, soc_range))
+    return reference
+
+
+def check_batch_results(jobs, reference, batched, n_threads):
+    """Compare one pk_run_batch call's results against the build_batch_reference() reference, returns True on failure"""
+    if len(batched) != len(jobs):
+        print("ERROR: pk_run_batch returned {} results for {} jobs".format(len(batched), len(jobs)))
+        return True
+
+    failed = False
+    for index, (result_tuple, soc_range_min, soc_range_max) in enumerate(batched):
+        single, (expect_min, expect_max) = reference[index]
+        if result_tuple is None:
+            print("ERROR: job {} reported a non-zero status at {} threads".format(index, n_threads))
+            failed = True
+            continue
+        for field, name in enumerate(RESULT_NAMES):
+            if result_tuple[field] != single[field]:
+                print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
+                failed = True
+        # The batch never materialises the SoC series - that is what makes it affordable
+        if result_tuple[11] != {}:
+            print("ERROR: job {} returned a SoC series from the batch path".format(index))
+            failed = True
+        for field, name in [(12, "car_charging_soc_next"), (13, "iboost_next"), (14, "iboost_running"), (15, "iboost_running_solar"), (16, "iboost_running_full")]:
+            if result_tuple[field] != single[field]:
+                print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
+                failed = True
+        if soc_range_min != expect_min or soc_range_max != expect_max:
+            print("ERROR: job {} at {} threads SoC range {} {} expected {} {}".format(index, n_threads, soc_range_min, soc_range_max, expect_min, expect_max))
+            failed = True
+    return failed
+
+
 def run_batch_parity_tests(my_predbat, count=60):
     """Check pk_run_batch matches a loop of pk_run exactly, at every thread count, returns True on failure.
 
@@ -905,61 +978,72 @@ def run_batch_parity_tests(my_predbat, count=60):
         print("SKIP: kernel does not expose pk_run_batch")
         return False
 
-    failed = False
     prediction, jobs = build_batch_jobs(my_predbat, random.Random(4321), count)
     if not prediction.kernel_handle:
         print("ERROR: batch parity kernel context creation failed")
         return True
 
-    # Reference: one pk_run per scenario, with the SoC series materialised so the range can be
-    # scanned in Python exactly as thread_run_prediction_charge_min_max does
-    reference = []
-    for job in jobs:
-        single = run_prediction_kernel(prediction, job.charge_limit, job.charge_window, job.export_window, job.export_limits, job.pv_scenario, job.end_record, 5, False)
-        if single is None:
-            print("ERROR: reference pk_run failed")
-            return True
-        if job.range_window is None:
-            soc_range = (prediction.soc_max, 0)
-        else:
-            soc_range = prediction.scan_soc_range(single[11], job.range_window)
-        reference.append((single, soc_range))
+    reference = build_batch_reference(prediction, jobs)
+    if reference is None:
+        return True
 
+    failed = False
     for n_threads in (1, 2, 4, 8):
         batched = prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads)
         if batched is None:
             print("ERROR: pk_run_batch failed at {} threads".format(n_threads))
             return True
-        if len(batched) != len(jobs):
-            print("ERROR: pk_run_batch returned {} results for {} jobs".format(len(batched), len(jobs)))
-            return True
-        for index, (result_tuple, soc_range_min, soc_range_max) in enumerate(batched):
-            single, (expect_min, expect_max) = reference[index]
-            if result_tuple is None:
-                print("ERROR: job {} reported a non-zero status at {} threads".format(index, n_threads))
-                failed = True
-                continue
-            for field, name in enumerate(RESULT_NAMES):
-                if result_tuple[field] != single[field]:
-                    print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
-                    failed = True
-            # The batch never materialises the SoC series - that is what makes it affordable
-            if result_tuple[11] != {}:
-                print("ERROR: job {} returned a SoC series from the batch path".format(index))
-                failed = True
-            for field, name in [(12, "car_charging_soc_next"), (13, "iboost_next"), (14, "iboost_running"), (15, "iboost_running_solar"), (16, "iboost_running_full")]:
-                if result_tuple[field] != single[field]:
-                    print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
-                    failed = True
-            if soc_range_min != expect_min or soc_range_max != expect_max:
-                print("ERROR: job {} at {} threads SoC range {} {} expected {} {}".format(index, n_threads, soc_range_min, soc_range_max, expect_min, expect_max))
-                failed = True
+        failed |= check_batch_results(jobs, reference, batched, n_threads)
 
     if not failed:
         print("Batch parity: {} scenarios bit-identical to pk_run at 1, 2, 4 and 8 threads".format(count))
     return failed
 
 
+def run_batch_window_variant_tests(my_predbat, count=30, window_variants=6):
+    """Check the batch's per-job window marshalling picks the right list, not a cached neighbour's.
+
+    build_batch_jobs's default (window_variants=1) shares one charge/export window list across the
+    whole batch, which exercises run_prediction_kernel_batch's window memo only on its cache-hit path:
+    every job asks for the same id(windows), so a memo keyed on anything coarser than list identity
+    would still pass. Task 4's real fan-out mixes windows freely between jobs, so here several distinct
+    window lists are cycled across the jobs (some jobs sharing a list, most not) and checked against the
+    same per-scenario pk_run reference run_batch_parity_tests uses - a job that picked up another list's
+    start/end arrays would simulate the wrong charge/export times and show up as a wrong result.
+    """
+    print("**** Running kernel batch window variant tests ****")
+    if not prediction_kernel.KERNEL_HAS_BATCH:
+        print("SKIP: kernel does not expose pk_run_batch")
+        return False
+
+    prediction, jobs = build_batch_jobs(my_predbat, random.Random(9876), count, window_variants=window_variants)
+    if not prediction.kernel_handle:
+        print("ERROR: batch window variant kernel context creation failed")
+        return True
+
+    distinct_charge = len({id(job.charge_window) for job in jobs})
+    distinct_export = len({id(job.export_window) for job in jobs})
+    if distinct_charge < 2 or distinct_export < 2:
+        print("ERROR: batch window variant test only produced {} charge and {} export window list(s) - not exercising the memo".format(distinct_charge, distinct_export))
+        return True
+
+    reference = build_batch_reference(prediction, jobs)
+    if reference is None:
+        return True
+
+    failed = False
+    for n_threads in (1, 4):
+        batched = prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads)
+        if batched is None:
+            print("ERROR: pk_run_batch failed at {} threads".format(n_threads))
+            return True
+        failed |= check_batch_results(jobs, reference, batched, n_threads)
+
+    if not failed:
+        print("Batch window variant parity: {} scenarios across {} charge and {} export window lists bit-identical to pk_run".format(count, distinct_charge, distinct_export))
+    return failed
+
+
 def kernel_available():
     """Ensure the kernel library is built and loaded, returns (available, required_failure)"""
     if not ensure_kernel_built():
@@ -1040,6 +1124,8 @@ def run_kernel_parity_tests(my_predbat):
             failed |= run_clipping_parity_tests(my_predbat)
         if not failed:
             failed |= run_batch_parity_tests(my_predbat)
+        if not failed:
+            failed |= run_batch_window_variant_tests(my_predbat)
     finally:
         restore_scenario_state(my_predbat, state)
 

From e29f9d1393f5801605dd59de6e21f18846d1c0ed Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 19:34:49 +0100
Subject: [PATCH 11/24] refactor(prediction): split trial-input building out of
 thread_run_prediction_*

Each thread_run_prediction_* built the trial inputs, ran the prediction and shaped the result. The
batch path needs the first and third but not the second, so they are split rather than copied - one
definition of what a trial input is, for both paths.

No-op: 20 random scenarios byte-identical.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/plan.py       |  2 --
 apps/predbat/prediction.py | 34 ++++++++++++++++++++++------------
 2 files changed, 22 insertions(+), 14 deletions(-)

diff --git a/apps/predbat/plan.py b/apps/predbat/plan.py
index 187e4ebab..915f2267e 100644
--- a/apps/predbat/plan.py
+++ b/apps/predbat/plan.py
@@ -672,8 +672,6 @@ def launch_run_prediction_single(self, charge_limit, charge_window, export_windo
         """
         Launch a thread to run a prediction
         """
-        charge_limit = list(charge_limit)
-        export_limits = list(export_limits)
         if self.pool and self.pool._state == "RUN":
             han = self.pool.apply_async(wrapped_run_prediction_single, (charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step))
         else:
diff --git a/apps/predbat/prediction.py b/apps/predbat/prediction.py
index 91c7bfb83..607d75e43 100644
--- a/apps/predbat/prediction.py
+++ b/apps/predbat/prediction.py
@@ -224,10 +224,30 @@ def __init__(self, base=None, pv_forecast_minute_step=None, pv_forecast_minute10
             # Store this dictionary in global so we can reconstruct it in the thread without passing the data
             PRED_GLOBAL["dict"] = self.__dict__.copy()
 
+    def _prepare_single(self, charge_limit, export_limits):
+        """Copy the caller's limit lists for a single-scenario trial - shared by thread_run_prediction_single and the batch path.
+
+        The copy used to live in Plan.launch_run_prediction_single, where it protected the caller's
+        lists from the pool pickling them; the batch path defers the read to flush time, which needs
+        the same protection, so it belongs with the other trial-input building.
+        """
+        return list(charge_limit), list(export_limits)
+
+    def _prepare_charge(self, try_soc, window_n, charge_limit, all_n):
+        """Build the trial charge limits - shared by thread_run_prediction_charge/_charge_min_max and the batch path"""
+        try_charge_limit = charge_limit.copy()
+        if all_n:
+            for set_n in all_n:
+                try_charge_limit[set_n] = try_soc
+        else:
+            try_charge_limit[window_n] = try_soc
+        return try_charge_limit
+
     def thread_run_prediction_single(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step):
         """
         Run single prediction in a thread
         """
+        charge_limit, export_limits = self._prepare_single(charge_limit, export_limits)
 
         (
             cost,
@@ -255,12 +275,7 @@ def thread_run_prediction_charge(self, try_soc, window_n, charge_limit, charge_w
         Run prediction in a thread
         """
 
-        try_charge_limit = charge_limit.copy()
-        if all_n:
-            for set_n in all_n:
-                try_charge_limit[set_n] = try_soc
-        else:
-            try_charge_limit[window_n] = try_soc
+        try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
 
         (
             cost,
@@ -319,12 +334,7 @@ def thread_run_prediction_charge_min_max(self, try_soc, window_n, charge_limit,
         Run prediction in a thread
         """
 
-        try_charge_limit = charge_limit.copy()
-        if all_n:
-            for set_n in all_n:
-                try_charge_limit[set_n] = try_soc
-        else:
-            try_charge_limit[window_n] = try_soc
+        try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
 
         (
             cost,

From c1f59cb875a23b5084a6043aa3c25ad2d0a14e93 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 19:50:19 +0100
Subject: [PATCH 12/24] perf(plan): batch the prediction fan-out into one
 kernel call

launch_run_prediction_* no longer runs anything: it prepares the trial inputs, queues a job and
returns a handle whose first get() flushes the whole batch through a single pk_run_batch call. Window
start/end arrays are marshalled once per fan-out rather than once per scenario, cache hits and
intra-batch duplicates are resolved in Python first, and anything the kernel refuses falls back to the
Python engine per job. Serial for now.

20 random scenarios byte-identical.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/plan.py                        |  75 ++-----
 apps/predbat/prediction.py                  |  57 +++--
 apps/predbat/prediction_batch.py            | 175 +++++++++++++++
 apps/predbat/tests/test_prediction_batch.py | 233 ++++++++++++++++++++
 4 files changed, 458 insertions(+), 82 deletions(-)
 create mode 100644 apps/predbat/prediction_batch.py

diff --git a/apps/predbat/plan.py b/apps/predbat/plan.py
index 915f2267e..dd7a806c3 100644
--- a/apps/predbat/plan.py
+++ b/apps/predbat/plan.py
@@ -20,11 +20,10 @@
 import copy
 import traceback
 from datetime import datetime, timedelta
-from multiprocessing import Pool, cpu_count
 from const import PREDICT_STEP, PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10, PV_SCENARIO_PV90, TIME_FORMAT, MINUTE_WATT
 
 from utils import calc_percent_limit, dp0, dp1, dp2, dp3, dp4, remove_intersecting_windows, in_car_slot
-from prediction import Prediction, wrapped_run_prediction_single, wrapped_run_prediction_charge, wrapped_run_prediction_charge_min_max, wrapped_run_prediction_export
+from prediction import Prediction
 from prediction_kernel import kernel_status_summary
 from predbat_metrics import metrics
 import time
@@ -669,47 +668,20 @@ def optimise_charge_limit_price_threads(
         )
 
     def launch_run_prediction_single(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step=PREDICT_STEP):
-        """
-        Launch a thread to run a prediction
-        """
-        if self.pool and self.pool._state == "RUN":
-            han = self.pool.apply_async(wrapped_run_prediction_single, (charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step))
-        else:
-            han = DummyThread(self.prediction.thread_run_prediction_single(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step))
-        return han
+        """Queue a prediction and return a handle to its result"""
+        return self.prediction.queue_run_prediction_single(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step)
 
     def launch_run_prediction_charge(self, loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """
-        Launch a thread to run a prediction
-        """
-        if self.pool and self.pool._state == "RUN":
-            han = self.pool.apply_async(wrapped_run_prediction_charge, (loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record))
-        else:
-            han = DummyThread(self.prediction.thread_run_prediction_charge(loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record))
-        return han
+        """Queue a prediction and return a handle to its result"""
+        return self.prediction.queue_run_prediction_charge(loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record)
 
     def launch_run_prediction_charge_min_max(self, loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """
-        Launch a thread to run a prediction
-        """
-        if self.pool and self.pool._state == "RUN":
-            han = self.pool.apply_async(wrapped_run_prediction_charge_min_max, (loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record))
-        else:
-            han = DummyThread(self.prediction.thread_run_prediction_charge_min_max(loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record))
-        return han
+        """Queue a prediction and return a handle to its result"""
+        return self.prediction.queue_run_prediction_charge_min_max(loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record)
 
     def launch_run_prediction_export(self, this_export_limit, start, window_n, try_charge_limit, charge_window, try_export_window, try_export, pv_scenario, all_n, end_record):
-        """
-        Launch a thread to run a prediction
-        """
-        if self.pool and self.pool._state == "RUN":
-            han = self.pool.apply_async(
-                wrapped_run_prediction_export,
-                (this_export_limit, start, window_n, try_charge_limit, charge_window, try_export_window, try_export, pv_scenario, all_n, end_record),
-            )
-        else:
-            han = DummyThread(self.prediction.thread_run_prediction_export(this_export_limit, start, window_n, try_charge_limit, charge_window, try_export_window, try_export, pv_scenario, all_n, end_record))
-        return han
+        """Queue a prediction and return a handle to its result"""
+        return self.prediction.queue_run_prediction_export(this_export_limit, start, window_n, try_charge_limit, charge_window, try_export_window, try_export, pv_scenario, all_n, end_record)
 
     def scenario_summary_title(self, record_time):
         txt = ""
@@ -1332,35 +1304,16 @@ def calculate_plan(self, recompute=True, debug_mode=False, publish=True):
 
         # Creation prediction object
         self.prediction = Prediction(self, pv_forecast_minute_step, pv_forecast_minute10_step, load_minutes_step, load_minutes_step10, pv_forecast_minute90_step, load_minutes_step90)
+        # Serial for now - Task 5 wires this to the threads config
+        self.prediction.batch_threads = 1
         kernel_message, kernel_is_warning = kernel_status_summary(self.prediction)
         self.log("{}Prediction kernel: {}".format("Warn: " if kernel_is_warning else "", kernel_message))
 
-        # Check if LoadML is active and disable thread pools as it causes lockup due to race conditions with NumPy
+        # Check if LoadML is active - it used to force the process pool off, which no longer exists;
+        # the kernel's threads are C++ threads with no fork and no NumPy involvement
         load_ml_comp = self.components.get_component("load_ml") if self.components else None
-        load_ml_calculating = False
         if load_ml_comp:
-            load_ml_calculating = load_ml_comp.is_calculating()
-            self.log("LoadML is_calculating {}".format(load_ml_calculating))
-            if load_ml_calculating and self.pool:
-                self.log("Disabling thread pool as LoadML is calculating to avoid lockups")
-                self.pool.close()
-                self.pool.join()
-                self.pool = None
-
-        # Create pool
-        if not self.pool:
-            if load_ml_calculating:
-                self.log("Not using thread pool as LoadML is calculating to avoid lockups")
-            else:
-                threads = self.get_arg("threads", "auto")
-                if threads == "auto":
-                    self.log("Creating pool of {} processes to match your CPU count".format(cpu_count()))
-                    self.pool = Pool(processes=cpu_count())
-                elif threads:
-                    self.log("Creating pool of {} processes as per apps.yaml".format(int(threads)))
-                    self.pool = Pool(processes=int(threads))
-                else:
-                    self.log("Not using threading as threads is set to 0 in apps.yaml")
+            self.log("LoadML is_calculating {}".format(load_ml_comp.is_calculating()))
 
         # Simulate current settings to get initial data
         metric, import_kwh_battery, import_kwh_house, export_kwh, soc_min, soc, soc_min_minute, battery_cycle, metric_keep, final_iboost, final_carbon_g = self.run_prediction(
diff --git a/apps/predbat/prediction.py b/apps/predbat/prediction.py
index 607d75e43..3d48daf4e 100644
--- a/apps/predbat/prediction.py
+++ b/apps/predbat/prediction.py
@@ -19,9 +19,9 @@
 
 from datetime import timedelta
 from const import PREDICT_STEP, PV_SCENARIO_PV10, PV_SCENARIO_PV90, RUN_EVERY, TIME_FORMAT
-from operator import itemgetter
 
 from utils import remove_intersecting_windows, get_charge_rate_curve_cached, get_discharge_rate_curve_cached, find_charge_rate, calc_percent_limit, in_iboost_slot, in_car_slot, charge_curve_to_tuple
+from prediction_batch import PredictionBatch, prediction_cache_key
 from prediction_kernel import create_kernel_context, kernel_supported, run_prediction_kernel
 
 
@@ -94,12 +94,7 @@ def get_total_inverted(battery_draw, pv_dc, pv_ac, inverter_loss, inverter_hybri
     return total_inverted
 
 
-# Pulls (start, end) from a window dict; used to build the prediction cache key without a Python
-# level loop over every window
-window_bounds = itemgetter("start", "end")
-
-
-class Prediction:
+class Prediction(PredictionBatch):
     """
     Class to hold prediction input and output data and the run function
     """
@@ -224,12 +219,17 @@ def __init__(self, base=None, pv_forecast_minute_step=None, pv_forecast_minute10
             # Store this dictionary in global so we can reconstruct it in the thread without passing the data
             PRED_GLOBAL["dict"] = self.__dict__.copy()
 
+            # Deliberately after the PRED_GLOBAL snapshot: a pool worker reconstructs its Prediction
+            # from that dict and never queues anything, so it has no business carrying a batch.
+            self.pending_batch = []
+            self.batch_threads = 1
+
     def _prepare_single(self, charge_limit, export_limits):
         """Copy the caller's limit lists for a single-scenario trial - shared by thread_run_prediction_single and the batch path.
 
-        The copy used to live in Plan.launch_run_prediction_single, where it protected the caller's
-        lists from the pool pickling them; the batch path defers the read to flush time, which needs
-        the same protection, so it belongs with the other trial-input building.
+        The copy used to live in Plan.launch_run_prediction_single. It is kept here because the batch
+        path does not read these lists until the batch is flushed, so a trial has to own the copy it
+        will eventually be simulated from rather than share the caller's list.
         """
         return list(charge_limit), list(export_limits)
 
@@ -270,6 +270,11 @@ def thread_run_prediction_single(self, charge_limit, charge_window, export_windo
         ) = self.run_prediction(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record=end_record, step=step, cache=self.prediction_cache_enable)
         return (cost, import_kwh_battery, import_kwh_house, export_kwh, soc_min, soc, soc_min_minute, battery_cycle, metric_keep, final_iboost, final_carbon_g)
 
+    def queue_run_prediction_single(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step):
+        """Queue a single-scenario prediction, returning a handle - the batch runs on the first get()"""
+        charge_limit, export_limits = self._prepare_single(charge_limit, export_limits)
+        return self.enqueue_prediction(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step, self.prediction_cache_enable)
+
     def thread_run_prediction_charge(self, try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
         """
         Run prediction in a thread
@@ -310,6 +315,11 @@ def thread_run_prediction_charge(self, try_soc, window_n, charge_limit, charge_w
             final_carbon_g,
         )
 
+    def queue_run_prediction_charge(self, try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
+        """Queue a charge-window trial prediction, returning a handle"""
+        try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
+        return self.enqueue_prediction(try_charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, PREDICT_STEP, self.prediction_cache_enable)
+
     def scan_soc_range(self, predict_soc, window):
         """Return the (min, max) SoC across a charge window - shared by the direct and batch min/max paths.
 
@@ -376,6 +386,16 @@ def thread_run_prediction_charge_min_max(self, try_soc, window_n, charge_limit,
             max_soc,
         )
 
+    def queue_run_prediction_charge_min_max(self, try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
+        """Queue a charge-window trial prediction that also reports the SoC range across that window.
+
+        Uncached, exactly as the direct path is: the SoC range is not part of the cached result, so a
+        hit would answer with the wrong shape.
+        """
+        try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
+        range_window = None if all_n else charge_window[window_n]
+        return self.enqueue_prediction(try_charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, PREDICT_STEP, False, want_range=True, range_window=range_window)
+
     def _prepare_export(self, this_export_limit, start, window_n, export_window, export_limits, all_n):
         """Build the trial export limits and window list - shared by thread_run_prediction_export and the batch path.
 
@@ -427,6 +447,11 @@ def thread_run_prediction_export(self, this_export_limit, start, window_n, charg
         ) = self.run_prediction(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record=end_record, cache=self.prediction_cache_enable)
         return metricmid, import_kwh_battery, import_kwh_house, export_kwh, soc_min, soc, soc_min_minute, battery_cycle, metric_keep, final_iboost, final_carbon_g
 
+    def queue_run_prediction_export(self, this_export_limit, start, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
+        """Queue an export-window trial prediction, returning a handle"""
+        export_window, export_limits = self._prepare_export(this_export_limit, start, window_n, export_window, export_limits, all_n)
+        return self.enqueue_prediction(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, PREDICT_STEP, self.prediction_cache_enable)
+
     def find_charge_window_optimised(self, charge_windows, charge_limit, is_export=False):
         """
         Takes in an array of charge windows
@@ -456,17 +481,7 @@ def run_prediction(self, charge_limit, charge_window, export_window, export_limi
         # every simulation with a few hundred windows.
         sim_hash = None
         if cache and not save:
-            sim_hash = hash(
-                (
-                    tuple(charge_limit),
-                    tuple(map(window_bounds, charge_window)),
-                    tuple(export_limits),
-                    tuple(map(window_bounds, export_window)),
-                    pv_scenario,
-                    end_record,
-                    step,
-                )
-            )
+            sim_hash = prediction_cache_key(charge_limit, charge_window, export_limits, export_window, pv_scenario, end_record, step)
             cached_result = self.prediction_cache.get(sim_hash)
             if cached_result is not None:
                 # Return cached result
diff --git a/apps/predbat/prediction_batch.py b/apps/predbat/prediction_batch.py
new file mode 100644
index 000000000..1f3152740
--- /dev/null
+++ b/apps/predbat/prediction_batch.py
@@ -0,0 +1,175 @@
+# -----------------------------------------------------------------------------
+# Predbat Home Battery System
+# Copyright Trefor Southwell 2026 - All Rights Reserved
+# This application maybe used for personal use only and not for commercial use
+# -----------------------------------------------------------------------------
+# fmt off
+# pylint: disable=consider-using-f-string
+# pylint: disable=line-too-long
+
+"""Lazy batching of the prediction fan-out.
+
+Every optimiser fan-out in plan.py is launch-all-then-collect-all, so the launches can queue instead
+of running: each one prepares its trial inputs, appends a BatchJob and returns a BatchHandle, and the
+first get() runs the whole batch through a single pk_run_batch call. That pays the Python/C boundary
+and the window marshalling once per fan-out instead of once per scenario, and it is the only shape
+that lets the kernel spread the work across cores - a Python thread pool cannot (the GIL serialises
+it) and a process pool cannot (the parent's pickling is a serial bottleneck); both were measured and
+rejected.
+
+INVARIANT: a caller must not mutate any list or window dict it has handed to a queue_* entry point
+until it has read the matching handle. The four fan-outs in plan.py satisfy this; the 20-scenario
+byte-identical plan comparison is what keeps it that way.
+"""
+
+from operator import itemgetter
+
+from const import PREDICT_STEP
+from prediction_kernel import BatchJob, kernel_supported, run_prediction_kernel_batch
+
+# Pulls (start, end) from a window dict; used to build the prediction cache key without a Python
+# level loop over every window
+window_bounds = itemgetter("start", "end")
+
+
+def prediction_cache_key(charge_limit, charge_window, export_limits, export_window, pv_scenario, end_record, step):
+    """Hash a simulation's inputs into its prediction cache key.
+
+    Shared by Prediction.run_prediction and the batch runner so a scenario cannot land under two
+    different keys depending on which path reached it. Built as one tuple hash to keep the per-window
+    hashing in C rather than looping in Python, which matters because it runs on every simulation
+    with a few hundred windows.
+    """
+    return hash(
+        (
+            tuple(charge_limit),
+            tuple(map(window_bounds, charge_window)),
+            tuple(export_limits),
+            tuple(map(window_bounds, export_window)),
+            pv_scenario,
+            end_record,
+            step,
+        )
+    )
+
+
+def shape_batch_result(job, result, soc_range_min, soc_range_max):
+    """Shape a full run_prediction result into what this job's thread_run_prediction_* returned"""
+    shaped = tuple(result[:11])
+    if job.want_range:
+        shaped = shaped + (soc_range_min, soc_range_max)
+    return shaped
+
+
+class BatchHandle:
+    """Result handle for a queued prediction - the drop-in for the pool's AsyncResult"""
+
+    __slots__ = ("pred", "job")
+
+    def __init__(self, pred, job):
+        """Bind the handle to the Prediction that owns the batch and to its own job"""
+        self.pred = pred
+        self.job = job
+
+    def get(self):
+        """Return this job's result, flushing the whole pending batch on the first call"""
+        if self.job.result is None:
+            self.pred.flush_batch()
+        return self.job.result
+
+
+class PredictionBatch:
+    """Pending-batch state and flushing for Prediction.
+
+    A mixin rather than a separate object so the pending list lives directly on the Prediction that
+    owns the kernel context, with no reference cycle between the two.
+    """
+
+    def enqueue_prediction(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step, cache, want_range=False, range_window=None):
+        """Queue one prediction and return a handle to its eventual result"""
+        sim_hash = prediction_cache_key(charge_limit, charge_window, export_limits, export_window, pv_scenario, end_record, step) if cache else None
+
+        # The kernel tracks the SoC range itself over a step range, so a min/max job does not have to
+        # ship an 8928-entry SoC buffer. A negative start step means "no range asked for", which the
+        # kernel answers with (soc_max, 0) - exactly what the Python all_n path returns unclamped.
+        soc_range_start_step = -1
+        soc_range_end_step = -1
+        if range_window is not None:
+            predict_minute_start = max(int((range_window["start"] - self.minutes_now) / 5) * 5, 0)
+            predict_minute_end = int((range_window["end"] - self.minutes_now) / 5) * 5
+            soc_range_start_step = predict_minute_start // PREDICT_STEP
+            soc_range_end_step = predict_minute_end // PREDICT_STEP
+
+        job = BatchJob(
+            charge_limit,
+            charge_window,
+            export_window,
+            export_limits,
+            pv_scenario,
+            end_record,
+            step,
+            soc_range_start_step=soc_range_start_step,
+            soc_range_end_step=soc_range_end_step,
+            sim_hash=sim_hash,
+            want_range=want_range,
+            range_window=range_window,
+        )
+        self.pending_batch.append(job)
+        return BatchHandle(self, job)
+
+    def run_batch_job_python(self, job):
+        """Run one job through the normal prediction path, for when the kernel cannot take it"""
+        result = self.run_prediction(job.charge_limit, job.charge_window, job.export_window, job.export_limits, job.pv_scenario, end_record=job.end_record, step=job.step, cache=job.sim_hash is not None)
+        if not job.want_range:
+            return tuple(result[:11])
+        if job.range_window is None:
+            return tuple(result[:11]) + (self.soc_max, 0)
+        return tuple(result[:11]) + self.scan_soc_range(result[11], job.range_window)
+
+    def flush_batch(self):
+        """Run every pending job: cache hits first, then one kernel call, then any fallbacks"""
+        pending = self.pending_batch
+        if not pending:
+            return
+        # Cleared before anything runs, so a fallback that re-enters run_prediction cannot see a
+        # half-flushed batch or flush it a second time
+        self.pending_batch = []
+
+        cache = self.prediction_cache
+        kernel_jobs = []
+        duplicates = []
+        first_by_hash = {}
+        for job in pending:
+            if job.sim_hash is not None:
+                cached = cache.get(job.sim_hash)
+                if cached is not None:
+                    job.result = shape_batch_result(job, cached, 0.0, 0.0)
+                    continue
+                # Two identical trials in one fan-out used to be a cache hit for the second one; the
+                # batch has to collapse them itself or that hit is lost (~44% of calls are hits)
+                first = first_by_hash.get(job.sim_hash)
+                if first is not None:
+                    duplicates.append((job, first))
+                    continue
+                first_by_hash[job.sim_hash] = job
+            kernel_jobs.append(job)
+
+        if kernel_jobs:
+            # kernel_supported() is a property of the Prediction, not of the individual job - it also
+            # refuses debug runs, which would otherwise reach the kernel here when the direct path
+            # would have used the Python engine. save is always None for a queued job: a saving run
+            # calls run_prediction directly and never fans out.
+            batched = run_prediction_kernel_batch(self, kernel_jobs, self.batch_threads) if kernel_supported(self, None, PREDICT_STEP) else None
+            for index, job in enumerate(kernel_jobs):
+                entry = batched[index] if batched is not None else None
+                if entry is None or entry[0] is None:
+                    job.result = self.run_batch_job_python(job)
+                    continue
+                result, soc_range_min, soc_range_max = entry
+                if job.sim_hash is not None:
+                    # Stored without the SoC/car data to save memory, mirroring run_prediction
+                    cache[job.sim_hash] = result[:11] + ([], []) + result[13:]
+                job.result = shape_batch_result(job, result, soc_range_min, soc_range_max)
+
+        for job, first in duplicates:
+            job.result = first.result
diff --git a/apps/predbat/tests/test_prediction_batch.py b/apps/predbat/tests/test_prediction_batch.py
index 059317aac..1affa757b 100644
--- a/apps/predbat/tests/test_prediction_batch.py
+++ b/apps/predbat/tests/test_prediction_batch.py
@@ -16,7 +16,13 @@
 """
 
 
+import random
+
+import prediction_batch
+from const import PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10
 from prediction import Prediction
+from prediction_kernel import create_kernel_context
+from tests.test_kernel_parity import apply_random_scenario, kernel_available, make_step_data, make_windows, restore_scenario_state, snapshot_scenario_state
 
 
 def make_export_windows(minutes_now):
@@ -72,9 +78,236 @@ def test_export_trial_does_not_mutate_caller_window(my_predbat):
     return failed
 
 
+def make_batch_prediction(my_predbat, seed=7):
+    """Build a kernel-enabled Prediction plus a charge/export window layout to fan out over"""
+    rng = random.Random(seed)
+    apply_random_scenario(my_predbat, rng)
+    pv_step, pv10_step, load_step, load10_step = make_step_data(my_predbat, rng)
+    my_predbat.prediction_kernel_enable = True
+    prediction = Prediction(my_predbat, pv_step, pv10_step, load_step, load10_step)
+    prediction.kernel_handle = create_kernel_context(prediction)
+    charge_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, 4)
+    export_window = make_windows(rng, my_predbat.minutes_now, my_predbat.forecast_minutes, 4)
+    charge_limit = [round(my_predbat.soc_max / 2, 2)] * len(charge_window)
+    export_limits = [100.0] * len(export_window)
+    return prediction, charge_window, export_window, charge_limit, export_limits
+
+
+def test_queued_matches_direct(my_predbat):
+    """Every queued prediction must return exactly what the direct thread path returns, returns True on failure"""
+    print("**** Running queued vs direct equivalence tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+    try_soc = round(my_predbat.soc_max * 0.8, 2)
+
+    cases = [
+        (
+            "single",
+            lambda: prediction.thread_run_prediction_single(charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, end_record, 5),
+            lambda: prediction.queue_run_prediction_single(charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, end_record, 5),
+        ),
+        (
+            "charge",
+            lambda: prediction.thread_run_prediction_charge(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_PV10, None, end_record),
+            lambda: prediction.queue_run_prediction_charge(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_PV10, None, end_record),
+        ),
+        (
+            "charge_min_max",
+            lambda: prediction.thread_run_prediction_charge_min_max(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record),
+            lambda: prediction.queue_run_prediction_charge_min_max(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record),
+        ),
+        (
+            "charge_min_max_all_n",
+            lambda: prediction.thread_run_prediction_charge_min_max(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, [0, 2], end_record),
+            lambda: prediction.queue_run_prediction_charge_min_max(try_soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, [0, 2], end_record),
+        ),
+        (
+            "export",
+            lambda: prediction.thread_run_prediction_export(5.0, export_window[1]["start"] + 15, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record),
+            lambda: prediction.queue_run_prediction_export(5.0, export_window[1]["start"] + 15, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record),
+        ),
+    ]
+
+    for name, direct_call, queue_call in cases:
+        # The queued run goes first, so the direct run cannot be answered out of the cache the queued
+        # one just populated - and vice versa, hence the clear between them
+        prediction.prediction_cache = {}
+        queued = queue_call().get()
+        prediction.prediction_cache = {}
+        direct = direct_call()
+        if queued != direct:
+            print("ERROR: {} queued {} != direct {}".format(name, queued, direct))
+            failed = True
+
+    if not failed:
+        print("Queued predictions match the direct path for all five shapes")
+    return failed
+
+
+def test_batch_is_lazy(my_predbat):
+    """Nothing runs until the first get(), and one get() flushes the whole batch, returns True on failure"""
+    print("**** Running batch laziness tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+
+    handles = [prediction.queue_run_prediction_charge(round(soc, 2), 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record) for soc in (1.0, 2.0, 3.0)]
+    if len(prediction.pending_batch) != 3:
+        print("ERROR: expected 3 pending jobs, got {}".format(len(prediction.pending_batch)))
+        failed = True
+    if any(handle.job.result is not None for handle in handles):
+        print("ERROR: a job ran before its handle was read")
+        failed = True
+
+    handles[0].get()
+    if prediction.pending_batch:
+        print("ERROR: pending batch not drained by the first get(), {} left".format(len(prediction.pending_batch)))
+        failed = True
+    if any(handle.job.result is None for handle in handles):
+        print("ERROR: one get() did not flush the whole batch")
+        failed = True
+
+    # A second read must not re-run anything
+    before = handles[2].job.result
+    if handles[2].get() is not before:
+        print("ERROR: reading a handle twice recomputed the result")
+        failed = True
+
+    if not failed:
+        print("Batch laziness tests passed")
+    return failed
+
+
+def test_batch_cache_and_dedup(my_predbat):
+    """Cache hits and intra-batch duplicates must never reach the kernel, returns True on failure"""
+    print("**** Running batch cache/dedup tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+    prediction.prediction_cache_enable = True
+
+    calls = []
+    real_batch = prediction_batch.run_prediction_kernel_batch
+
+    def spy(pred, jobs, n_threads):
+        """Record how many jobs each batch call was handed, then run it for real"""
+        calls.append(len(jobs))
+        return real_batch(pred, jobs, n_threads)
+
+    prediction_batch.run_prediction_kernel_batch = spy
+    try:
+        # Three identical jobs in one batch must collapse to one kernel job
+        prediction.prediction_cache = {}
+        handles = [prediction.queue_run_prediction_charge(4.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record) for _ in range(3)]
+        results = [handle.get() for handle in handles]
+        if calls != [1]:
+            print("ERROR: expected one kernel job for three identical trials, got {}".format(calls))
+            failed = True
+        if results[0] != results[1] or results[1] != results[2]:
+            print("ERROR: deduplicated jobs returned different results")
+            failed = True
+
+        # And a repeat in a later batch must come from the cache without a kernel call at all
+        del calls[:]
+        again = prediction.queue_run_prediction_charge(4.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+        if calls:
+            print("ERROR: a cached scenario still reached the kernel: {}".format(calls))
+            failed = True
+        if again != results[0]:
+            print("ERROR: cached result {} differs from the computed one {}".format(again, results[0]))
+            failed = True
+    finally:
+        prediction_batch.run_prediction_kernel_batch = real_batch
+
+    if not failed:
+        print("Batch cache/dedup tests passed")
+    return failed
+
+
+def test_batch_fallbacks(my_predbat):
+    """A job the kernel cannot take still runs, and gives the Python engine's answer, returns True on failure"""
+    print("**** Running batch fallback tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+
+    # Two references, because the fallbacks land on two different engines. A batch the kernel refused
+    # still re-runs through run_prediction, which dispatches to the kernel; a Prediction the kernel
+    # will not serve at all re-runs on the Python engine. Those two agree to 1e-6 (kernel_parity), not
+    # bit-exactly, so each fallback is compared against the engine it actually used.
+    prediction.prediction_cache = {}
+    expected = prediction.thread_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record)
+    prediction.debug_enable = True
+    prediction.prediction_cache = {}
+    expected_python = prediction.thread_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record)
+    prediction.debug_enable = False
+
+    # 1. Whole batch refused (no kernel, stale binary, or a non-zero return code)
+    real_batch = prediction_batch.run_prediction_kernel_batch
+
+    def refuse_all(pred, jobs, n_threads):
+        """Stand in for a kernel that cannot run the batch at all"""
+        return None
+
+    def refuse_each(pred, jobs, n_threads):
+        """Stand in for a kernel that ran the batch but rejected every job"""
+        return [(None, 0.0, 0.0)] * len(jobs)
+
+    for name, stub in (("whole batch", refuse_all), ("per job", refuse_each)):
+        prediction_batch.run_prediction_kernel_batch = stub
+        try:
+            prediction.prediction_cache = {}
+            got = prediction.queue_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+            if got != expected:
+                print("ERROR: {} fallback returned {} expected {}".format(name, got, expected))
+                failed = True
+        finally:
+            prediction_batch.run_prediction_kernel_batch = real_batch
+
+    # 2. Debug runs must not reach the kernel from the batch when the direct path would not take it
+    prediction.debug_enable = True
+    try:
+        prediction.prediction_cache = {}
+        got = prediction.queue_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+        if got != expected_python:
+            print("ERROR: debug_enable fallback returned {} expected {}".format(got, expected_python))
+            failed = True
+    finally:
+        prediction.debug_enable = False
+
+    # 3. Kernel genuinely unavailable for this Prediction
+    prediction.kernel_handle = 0
+    prediction.prediction_cache = {}
+    got = prediction.queue_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+    if got != expected_python:
+        print("ERROR: kernel-less fallback returned {} expected {}".format(got, expected_python))
+        failed = True
+
+    if not failed:
+        print("Batch fallback tests passed")
+    return failed
+
+
 def run_prediction_batch_tests(my_predbat):
     """Run every batched prediction test, returns True on failure"""
     failed = test_export_trial_does_not_mutate_caller_window(my_predbat)
+
+    available, required_failure = kernel_available()
+    if not available:
+        print("WARNING: kernel not available - batch tests that need it are SKIPPED")
+        return failed or required_failure
+
+    state = snapshot_scenario_state(my_predbat)
+    try:
+        failed |= test_queued_matches_direct(my_predbat)
+        failed |= test_batch_is_lazy(my_predbat)
+        failed |= test_batch_cache_and_dedup(my_predbat)
+        failed |= test_batch_fallbacks(my_predbat)
+    finally:
+        restore_scenario_state(my_predbat, state)
+        my_predbat.prediction_kernel_enable = False
+
     if failed:
         print("**** Prediction batch tests FAILED ****")
     else:

From 267516974d9a1efbfad1f6f50750315f56029a6a Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 20:07:00 +0100
Subject: [PATCH 13/24] test(batch): pin the debug kernel gate by mechanism,
 not by value

The debug_enable case of test_batch_fallbacks asserted a result value, which the two engines agree on
to ~1e-6 with every field rounded to 4dp - so it passed whether or not the kernel_supported() gate in
flush_batch existed, leaving the one guard that keeps debug runs on the Python engine unpinned. It now
spies on run_prediction_kernel_batch and asserts the kernel was never reached, keeping the value
assertion alongside.

Also: make "a min/max job is never cached" structural rather than a property of the single caller that
passes cache=False, since a cache hit on a range job would answer (0.0, 0.0) and silently collapse the
optimiser's SoC pruning envelope; cover a range window that starts before minutes_now (the max(...,0)
clamp and the wholly-past negative end step were never executed); cover a single prediction at the
levels optimiser's coarse step; and fix the garbled module docstring.

20 random scenarios still byte-identical.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/prediction_batch.py            |   6 +-
 apps/predbat/tests/test_prediction_batch.py | 101 ++++++++++++++++++--
 2 files changed, 100 insertions(+), 7 deletions(-)

diff --git a/apps/predbat/prediction_batch.py b/apps/predbat/prediction_batch.py
index 1f3152740..7694a4e39 100644
--- a/apps/predbat/prediction_batch.py
+++ b/apps/predbat/prediction_batch.py
@@ -87,7 +87,11 @@ class PredictionBatch:
 
     def enqueue_prediction(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step, cache, want_range=False, range_window=None):
         """Queue one prediction and return a handle to its eventual result"""
-        sim_hash = prediction_cache_key(charge_limit, charge_window, export_limits, export_window, pv_scenario, end_record, step) if cache else None
+        # A range job is never cached, and that is structural rather than the caller's choice: the
+        # SoC range is not part of a cached result, so a hit would answer with (0.0, 0.0) and silently
+        # collapse the caller's SoC pruning envelope. Callers pass cache=False today; ignoring cache
+        # outright for a range job means a future caller cannot reintroduce that bug.
+        sim_hash = prediction_cache_key(charge_limit, charge_window, export_limits, export_window, pv_scenario, end_record, step) if cache and not want_range else None
 
         # The kernel tracks the SoC range itself over a step range, so a min/max job does not have to
         # ship an 8928-entry SoC buffer. A negative start step means "no range asked for", which the
diff --git a/apps/predbat/tests/test_prediction_batch.py b/apps/predbat/tests/test_prediction_batch.py
index 1affa757b..4305f9c00 100644
--- a/apps/predbat/tests/test_prediction_batch.py
+++ b/apps/predbat/tests/test_prediction_batch.py
@@ -10,16 +10,19 @@
 """Tests for the batched prediction fan-out.
 
 launch_run_prediction_* no longer runs anything: it queues a job on the Prediction and returns a
-handle whose first get() flushes the whole batch through one pk_run_batch call. These tests pin the
-two things that makes conditional - that a queued job returns exactly what the direct
-thread_run_prediction_* path returns, and that a job the kernel cannot take still runs.
+handle whose first get() flushes the whole batch through one pk_run_batch call. These tests pin what
+that deferral relies on: that a trial owns its inputs rather than sharing the caller's, that a queued
+job returns exactly what the direct thread_run_prediction_* path returns for every job shape and step,
+that nothing runs before the first get() and one get() drains the batch, that cache hits and
+intra-batch duplicates never reach the kernel while min/max jobs are never cached at all, and that
+every job the kernel will not take still runs on the engine the direct path would have used.
 """
 
 
 import random
 
 import prediction_batch
-from const import PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10
+from const import PREDICT_STEP, PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10
 from prediction import Prediction
 from prediction_kernel import create_kernel_context
 from tests.test_kernel_parity import apply_random_scenario, kernel_available, make_step_data, make_windows, restore_scenario_state, snapshot_scenario_state
@@ -127,6 +130,14 @@ def test_queued_matches_direct(my_predbat):
             lambda: prediction.thread_run_prediction_export(5.0, export_window[1]["start"] + 15, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record),
             lambda: prediction.queue_run_prediction_export(5.0, export_window[1]["start"] + 15, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record),
         ),
+        # The levels optimiser runs single predictions at a coarse "fast mode" step (plan_interval_minutes,
+        # 30 by default), which is the only caller that passes a step other than PREDICT_STEP. It reaches
+        # both the cache key and the job, so it needs its own case
+        (
+            "single_coarse_step",
+            lambda: prediction.thread_run_prediction_single(charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, end_record, 30),
+            lambda: prediction.queue_run_prediction_single(charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, end_record, 30),
+        ),
     ]
 
     for name, direct_call, queue_call in cases:
@@ -141,7 +152,7 @@ def test_queued_matches_direct(my_predbat):
             failed = True
 
     if not failed:
-        print("Queued predictions match the direct path for all five shapes")
+        print("Queued predictions match the direct path for all {} shapes".format(len(cases)))
     return failed
 
 
@@ -217,6 +228,27 @@ def spy(pred, jobs, n_threads):
         if again != results[0]:
             print("ERROR: cached result {} differs from the computed one {}".format(again, results[0]))
             failed = True
+
+        # A min/max job must never be cached. The SoC range is not part of a cached result, so a hit
+        # would answer (0.0, 0.0), collapse the caller's SoC pruning envelope and silently drop
+        # candidate SoCs from the search. Checked twice: through the public entry point, and by
+        # asking enqueue_prediction for a cached range job directly - the second is what pins the
+        # guard as structural rather than a property of the one call site that passes cache=False.
+        prediction.prediction_cache = {}
+        for _ in range(2):
+            prediction.queue_run_prediction_charge_min_max(4.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+        if prediction.prediction_cache:
+            print("ERROR: a queued min/max job populated the prediction cache: {}".format(len(prediction.prediction_cache)))
+            failed = True
+
+        range_handle = prediction.enqueue_prediction(list(charge_limit), charge_window, export_window, list(export_limits), PV_SCENARIO_NOMINAL, end_record, PREDICT_STEP, True, want_range=True, range_window=charge_window[1])
+        if range_handle.job.sim_hash is not None:
+            print("ERROR: a range job was given a cache key despite never being cacheable")
+            failed = True
+        range_handle.get()
+        if prediction.prediction_cache:
+            print("ERROR: a range job enqueued with cache=True populated the prediction cache: {}".format(len(prediction.prediction_cache)))
+            failed = True
     finally:
         prediction_batch.run_prediction_kernel_batch = real_batch
 
@@ -225,6 +257,46 @@ def spy(pred, jobs, n_threads):
     return failed
 
 
+def test_queued_range_window_in_the_past(my_predbat):
+    """A range window that started before minutes_now must give the direct path's answer, returns True on failure.
+
+    make_windows only ever emits windows starting at or after minutes_now, so the max(..., 0) clamp on
+    the range start step and the negative end step of a wholly-past window are otherwise never
+    executed. Both matter: the kernel derives the range from those two step numbers while a fallback
+    derives it from scan_soc_range, and the two have to agree exactly.
+    """
+    print("**** Running past range window tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+    minutes_now = my_predbat.minutes_now
+    window = charge_window[1]
+
+    cases = [
+        # Straddles minutes_now: the start step clamps to 0, the end step is positive
+        ("straddling", {"start": minutes_now - 120, "end": window["end"], "average": window["average"]}),
+        # Wholly behind minutes_now: both steps are negative, so no step is ever in range
+        ("wholly past", {"start": minutes_now - 240, "end": minutes_now - 60, "average": window["average"]}),
+        # Ends exactly at minutes_now, the boundary between the two cases above
+        ("ends at now", {"start": minutes_now - 180, "end": minutes_now, "average": window["average"]}),
+    ]
+
+    for name, past_window in cases:
+        trial_window = list(charge_window)
+        trial_window[1] = past_window
+        prediction.prediction_cache = {}
+        queued = prediction.queue_run_prediction_charge_min_max(3.0, 1, charge_limit, trial_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+        prediction.prediction_cache = {}
+        direct = prediction.thread_run_prediction_charge_min_max(3.0, 1, charge_limit, trial_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record)
+        if queued != direct:
+            print("ERROR: {} range window queued {} != direct {}".format(name, queued, direct))
+            failed = True
+
+    if not failed:
+        print("Past range window tests passed for all three shapes")
+    return failed
+
+
 def test_batch_fallbacks(my_predbat):
     """A job the kernel cannot take still runs, and gives the Python engine's answer, returns True on failure"""
     print("**** Running batch fallback tests ****")
@@ -265,15 +337,31 @@ def refuse_each(pred, jobs, n_threads):
         finally:
             prediction_batch.run_prediction_kernel_batch = real_batch
 
-    # 2. Debug runs must not reach the kernel from the batch when the direct path would not take it
+    # 2. Debug runs must not reach the kernel from the batch when the direct path would not take it.
+    # This has to assert the mechanism, not the value: the two engines agree to ~1e-6 and every field
+    # is rounded to 4dp, so the tuples match whether or not the kernel_supported() gate exists. The
+    # gate is load-bearing because a debug run needs predict_soc_best and friends populated for the
+    # debug HTML, and reset_kernel_run_state clears exactly those.
+    calls = []
+
+    def spy_batch(pred, jobs, n_threads):
+        """Record that the kernel batch was reached at all, then run it for real"""
+        calls.append(len(jobs))
+        return real_batch(pred, jobs, n_threads)
+
     prediction.debug_enable = True
+    prediction_batch.run_prediction_kernel_batch = spy_batch
     try:
         prediction.prediction_cache = {}
         got = prediction.queue_run_prediction_charge_min_max(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record).get()
+        if calls:
+            print("ERROR: a debug run reached the kernel batch: {}".format(calls))
+            failed = True
         if got != expected_python:
             print("ERROR: debug_enable fallback returned {} expected {}".format(got, expected_python))
             failed = True
     finally:
+        prediction_batch.run_prediction_kernel_batch = real_batch
         prediction.debug_enable = False
 
     # 3. Kernel genuinely unavailable for this Prediction
@@ -301,6 +389,7 @@ def run_prediction_batch_tests(my_predbat):
     state = snapshot_scenario_state(my_predbat)
     try:
         failed |= test_queued_matches_direct(my_predbat)
+        failed |= test_queued_range_window_in_the_past(my_predbat)
         failed |= test_batch_is_lazy(my_predbat)
         failed |= test_batch_cache_and_dedup(my_predbat)
         failed |= test_batch_fallbacks(my_predbat)

From a0b8059a66d57c72ab2c2b16df5715d2c4265a59 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 20:25:22 +0100
Subject: [PATCH 14/24] perf(plan): run each batched fan-out across kernel
 threads

args[threads] now sizes the kernel's thread pool rather than a pool of worker processes. The GIL is
released for the whole pk_run_batch call, so unlike a Python ThreadPool these are real cores.

Also corrects a stale comment in annual.py that still described calculate_plan() as spinning up a
multiprocessing Pool; it no longer does, and forcing threads=0 there is now about skipping the C++
kernel's per-call std::thread pool creation on hundreds of tiny per-day plans, not fork safety.

Identical results at 1, 2 and 8 threads; 20 random scenarios byte-identical with threading actually
on (16 threads, this machine's CPU count).

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/annual.py                      | 16 +++++-----
 apps/predbat/plan.py                        | 12 ++++++--
 apps/predbat/tests/test_prediction_batch.py | 34 +++++++++++++++++++++
 docs/apps-yaml.md                           |  4 +++
 4 files changed, 56 insertions(+), 10 deletions(-)

diff --git a/apps/predbat/annual.py b/apps/predbat/annual.py
index 1d9c1413c..3e133c482 100644
--- a/apps/predbat/annual.py
+++ b/apps/predbat/annual.py
@@ -997,14 +997,14 @@ def prepare_sample(predbat, config, weather, tariff, load_source, day, midnight_
     """
     reset_sample_state(predbat)
 
-    # calculate_plan() spins up a multiprocessing Pool sized from args["threads"] (plan.py)
-    # unless it is exactly 0. The annual tool plans hundreds of individual days per run, each a
-    # small, fast calculation, so per-day pool creation is both wasted overhead and, on a
-    # spawn-based multiprocessing start method, unsafe: pool workers read the module-level
-    # PRED_GLOBAL dict in prediction.py, which is only populated in the parent process. This
-    # matches create_headless_predbat()'s own choice for a fully offline run; it is forced here
-    # too so run_day() behaves the same way against a caller-supplied PredBat instance (such as
-    # the standard unit test fixture) that has not gone through that bootstrap.
+    # calculate_plan() sizes the C++ kernel's batch thread count from args["threads"] (plan.py); it
+    # no longer spins up a Python multiprocessing Pool. pk_run_batch creates and joins its own
+    # std::thread pool fresh on every call, so leaving threads at its 'auto' default would pay that
+    # creation cost on each of the hundreds of small, fast per-day calculations the annual tool runs
+    # - overhead a plan this size cannot recoup. This matches create_headless_predbat()'s own choice
+    # for a fully offline run; it is forced here too so run_day() behaves the same way against a
+    # caller-supplied PredBat instance (such as the standard unit test fixture) that has not gone
+    # through that bootstrap.
     predbat.args["threads"] = 0
 
     predbat.midnight_utc = midnight_utc
diff --git a/apps/predbat/plan.py b/apps/predbat/plan.py
index dd7a806c3..c0fff625c 100644
--- a/apps/predbat/plan.py
+++ b/apps/predbat/plan.py
@@ -20,6 +20,7 @@
 import copy
 import traceback
 from datetime import datetime, timedelta
+from multiprocessing import cpu_count
 from const import PREDICT_STEP, PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10, PV_SCENARIO_PV90, TIME_FORMAT, MINUTE_WATT
 
 from utils import calc_percent_limit, dp0, dp1, dp2, dp3, dp4, remove_intersecting_windows, in_car_slot
@@ -1304,8 +1305,15 @@ def calculate_plan(self, recompute=True, debug_mode=False, publish=True):
 
         # Creation prediction object
         self.prediction = Prediction(self, pv_forecast_minute_step, pv_forecast_minute10_step, load_minutes_step, load_minutes_step10, pv_forecast_minute90_step, load_minutes_step90)
-        # Serial for now - Task 5 wires this to the threads config
-        self.prediction.batch_threads = 1
+        # The kernel spreads one batched fan-out across threads with the GIL released for the whole
+        # call, so these are real cores - unlike a Python ThreadPool, which peaked at 1.15x on two
+        # threads and then degraded below serial (perf/threadpool-prototype).
+        threads = self.get_arg("threads", "auto")
+        if threads == "auto":
+            self.prediction.batch_threads = cpu_count()
+        else:
+            self.prediction.batch_threads = max(int(threads), 1)
+        self.log("Prediction batch using {} kernel thread(s)".format(self.prediction.batch_threads))
         kernel_message, kernel_is_warning = kernel_status_summary(self.prediction)
         self.log("{}Prediction kernel: {}".format("Warn: " if kernel_is_warning else "", kernel_message))
 
diff --git a/apps/predbat/tests/test_prediction_batch.py b/apps/predbat/tests/test_prediction_batch.py
index 4305f9c00..9198e879d 100644
--- a/apps/predbat/tests/test_prediction_batch.py
+++ b/apps/predbat/tests/test_prediction_batch.py
@@ -377,6 +377,39 @@ def spy_batch(pred, jobs, n_threads):
     return failed
 
 
+def test_batch_threads_do_not_change_results(my_predbat):
+    """A threaded flush must return exactly what a serial one does, returns True on failure.
+
+    pk_run_batch is pinned bit-identical across thread counts at the C level (kernel_parity), so this
+    is really checking the Python side: that results are matched back to their own job regardless of
+    how the kernel split the work.
+    """
+    print("**** Running batch threading tests ****")
+    failed = False
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
+    end_record = my_predbat.forecast_minutes
+    socs = [round(my_predbat.soc_max * fraction, 2) for fraction in (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0)]
+
+    reference = None
+    for n_threads in (1, 2, 8):
+        prediction.batch_threads = n_threads
+        prediction.prediction_cache = {}
+        handles = [prediction.queue_run_prediction_charge(soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record) for soc in socs]
+        results = [handle.get() for handle in handles]
+        if reference is None:
+            reference = results
+        elif results != reference:
+            for index, (got, want) in enumerate(zip(results, reference)):
+                if got != want:
+                    print("ERROR: job {} at {} threads returned {} expected {}".format(index, n_threads, got, want))
+            failed = True
+    prediction.batch_threads = 1
+
+    if not failed:
+        print("Batch threading tests passed - identical results at 1, 2 and 8 threads")
+    return failed
+
+
 def run_prediction_batch_tests(my_predbat):
     """Run every batched prediction test, returns True on failure"""
     failed = test_export_trial_does_not_mutate_caller_window(my_predbat)
@@ -393,6 +426,7 @@ def run_prediction_batch_tests(my_predbat):
         failed |= test_batch_is_lazy(my_predbat)
         failed |= test_batch_cache_and_dedup(my_predbat)
         failed |= test_batch_fallbacks(my_predbat)
+        failed |= test_batch_threads_do_not_change_results(my_predbat)
     finally:
         restore_scenario_state(my_predbat, state)
         my_predbat.prediction_kernel_enable = False
diff --git a/docs/apps-yaml.md b/docs/apps-yaml.md
index 40dc6e55b..27022a907 100644
--- a/docs/apps-yaml.md
+++ b/docs/apps-yaml.md
@@ -286,6 +286,10 @@ This will remove the need for a DNS lookup of the IP address every time Predbat
 
 If defined sets the number of threads to use during plan calculation, the default is 'auto' which will use the same number of threads as you have CPUs in your system.
 
+Predbat batches each group of simulations into a single call to the C++ prediction kernel, which then
+spreads them across this many threads. Results do not depend on the thread count - the same plan is
+produced at any setting - so this only trades CPU for planning time.
+
 Valid values are:
 
 - 'auto' - Use the same number of threads as your CPU count

From 8e2c0e5cfc8f5a3155e7b1119f0abe5b26c691a4 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 20:43:28 +0100
Subject: [PATCH 15/24] refactor(plan): remove the prediction process pool

Nothing has launched work into it since the fan-out started batching, so PRED_GLOBAL, the four
wrapped_run_prediction_* trampolines, DummyThread and the pool lifecycle all go with it. The pool was
measured at 1.11x on 16 cores, with the parent's per-call pickling as the serial bottleneck.

Also cleans up the code and comments left pointing at the removed pool: cleanup_pool() and its call
sites in predbat.py, the stale "pool" entry in userinterface.py's debug-dump exclude list, and
pool-specific reasoning in comments in hass.py and load_ml_component.py. Two tests that used
DummyThread purely as a generic result-handle double (not to exercise pool behaviour) now use a small
local FakeHandle in test_pv90.py; test_infra.py's simple_scenario() calls
Prediction.thread_run_prediction_single directly instead of the deleted wrapper.

20 random scenarios byte-identical.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 PHASE_B_BATCH_REFACTOR.md                    | 227 -------------------
 apps/predbat/hass.py                         |   4 +-
 apps/predbat/load_ml_component.py            |   6 +-
 apps/predbat/marginal.py                     |   3 +-
 apps/predbat/plan.py                         |  31 ---
 apps/predbat/predbat.py                      |  42 ----
 apps/predbat/prediction.py                   |  48 ----
 apps/predbat/tests/test_download.py          |   2 -
 apps/predbat/tests/test_export_commitment.py |   1 -
 apps/predbat/tests/test_infra.py             |   4 +-
 apps/predbat/tests/test_pv90.py              |  25 +-
 apps/predbat/userinterface.py                |   1 -
 12 files changed, 28 insertions(+), 366 deletions(-)
 delete mode 100644 PHASE_B_BATCH_REFACTOR.md

diff --git a/PHASE_B_BATCH_REFACTOR.md b/PHASE_B_BATCH_REFACTOR.md
deleted file mode 100644
index 7d98291cf..000000000
--- a/PHASE_B_BATCH_REFACTOR.md
+++ /dev/null
@@ -1,227 +0,0 @@
-# Phase B: batch the prediction fan-out from Python
-
-Working notes for picking this up in a fresh session. Delete this file before the branch merges.
-
-Branch: `perf/kernel-batch-abi` (4 commits, unpushed, based on `main`).
-
-## Where things stand
-
-The C++ side is done, verified and committed. The Python side has not been touched yet — it still
-issues one `pk_run` call per scenario.
-
-| commit | what |
-|---|---|
-| `a3254924` | `pk_run_batch` ABI added; stop filling the SoC series when the caller discards it |
-| `2e71022e` | precomputed SoC percent buckets replace `round_py` in the hot loop |
-| `be2510d0` | bucket table verification test; all 6 shipped binaries rebuilt |
-| `f813d219` | fix: don't clamp the batch SoC range when none was requested |
-
-Everything is byte-identical to before: all 20 random scenarios unchanged on metric, cost and all
-three PV futures, `kernel_parity` green, full suite green including slow tests (101.22s).
-
-### What the kernel work bought
-
-Measured on `cases/random_scenarios.yaml` scenario 0 (the benchmark scenario), `threads=0`:
-
-```text
-calculate_plan   2418.4 ms  ->  1758.5 ms     -27%
-  C++ pk_run     1056.5 ms  ->   380.5 ms
-  Python         1361.9 ms  ->  1378.0 ms     unchanged, as expected
-C++ share            43.7%  ->     21.6%
-```
-
-20-scenario benchmark: **42.5s -> 31.6s**.
-
-## Why Phase B is now the whole game
-
-**Python is 78.4% of plan time.** The kernel is no longer the bottleneck by a wide margin — per
-prediction it is roughly 20us of C++ against ~70us of Python. Every further optimisation has to come
-off the Python side, and batching is the mechanism.
-
-There is a second, independent reason: `pk_run_batch` already takes an `n_threads` argument and
-scales properly now (**6.7x at 16 threads, bit-identical at every thread count**), but nothing can
-use it until Python submits work in batches. One `pk_run` call per scenario cannot be parallelised —
-that was measured and rejected twice, see "Dead ends" below.
-
-## The design
-
-### The call sites are already batch-shaped
-
-This is the key enabler and the reason this refactor is tractable. Every fan-out in `plan.py` was
-written for the process pool, so it already reads launch-all-then-collect-all:
-
-```python
-for try_soc in try_socs:
-    results.append(self.launch_run_prediction_charge(...))   # fan out
-for try_soc in try_socs:
-    resultmid[try_soc] = results.pop(0).get()                # then collect
-```
-
-`DummyThread` exists purely so the non-pooled path mimics that shape. So **the call sites do not need
-to change at all** — only what `launch_*` returns and when the work actually happens.
-
-### Lazy batch
-
-- `launch_run_prediction_*` stops running anything. It appends a job to a pending list on the
-  `Prediction` and returns a `BatchHandle(batch, index)`.
-- `BatchHandle.get()` flushes the whole pending batch on first call, then returns its own slot.
-  Subsequent `get()`s read already-computed results.
-- Flush: resolve prediction-cache hits in Python first, marshal only the misses into a
-  `PkBatchJob` array, one `pk_run_batch` call, then shape each job's result.
-
-Batch sizes this produces (scenario 0, from the profile):
-
-```text
-launch_run_prediction_charge          10,698
-launch_run_prediction_export           5,436
-launch_run_prediction_single           4,752
-launch_run_prediction_charge_min_max   3,294
-                                      ------
-                                      24,180 launches, ~19,209 reaching the kernel
-```
-
-`optimise_charge_limit_price_threads` fans out ~1176 at a time (392 candidates x 3 PV scenarios),
-`optimise_charge_limit` ~900, `optimise_export` ~628. Those are the batches.
-
-### Do not duplicate the input preparation
-
-Each `thread_run_prediction_*` does three things: build the trial inputs, call `run_prediction`,
-shape the result. The batch path needs the first and third but not the second.
-
-**Split them rather than copying them**, so the direct and batch paths cannot drift:
-
-```python
-def _prepare_charge(self, try_soc, window_n, charge_limit, all_n):
-    """Build the trial charge limits - shared by thread_run_prediction_charge and the batch path."""
-    try_charge_limit = charge_limit.copy()
-    if all_n:
-        for set_n in all_n:
-            try_charge_limit[set_n] = try_soc
-    else:
-        try_charge_limit[window_n] = try_soc
-    return try_charge_limit
-```
-
-Same for export (`export_limits.copy()` plus the private window copy — see traps) and single
-(pass-through). Then `thread_run_prediction_*` becomes prepare + `run_prediction` + shape, and the
-batch enqueue is prepare + record.
-
-### min/max jobs
-
-`thread_run_prediction_charge_min_max` scans `predict_soc` across the charge window to get
-`min_soc`/`max_soc`. The kernel now does this itself so the batch does not have to ship an
-8928-entry SoC buffer per job (that would be ~84MB on a large batch). Set on the job:
-
-```python
-predict_minute_start = max(int((window["start"] - self.minutes_now) / 5) * 5, 0)
-predict_minute_end = int((window["end"] - self.minutes_now) / 5) * 5
-job.soc_range_start_step = predict_minute_start // 5
-job.soc_range_end_step = predict_minute_end // 5      # inclusive
-```
-
-and read `result.soc_range_min` / `result.soc_range_max` back.
-
-**When `all_n` is set, Python skips the scan and returns `(soc_max, 0)` unclamped.** Pass
-`soc_range_start_step = -1` for those; the kernel returns exactly `(soc_max, 0)` in that case (this
-is what `f813d219` fixed).
-
-### Fallbacks that must keep working
-
-The batch path only applies when the kernel can run the job. Fall back to running the job through
-the existing `thread_run_prediction_*` path, serially, when any of:
-
-- `KERNEL_HAS_BATCH` is False (binary predates `pk_run_batch` — the loader probes for the symbol)
-- `kernel_supported()` is False for the job (no kernel context, `debug_enable`, or a `save` run)
-- `pk_run_batch` returns non-zero, or a job's `status` field is non-zero
-
-A per-job `status` is returned precisely so one malformed scenario cannot discard the fan-out.
-
-## Traps found the hard way
-
-1. **`thread_run_prediction_export` used to mutate the caller's window dict** (`export_window[n]["start"] = start`).
-   That was only safe because each pool worker mutated its own unpickled copy. Any shared-memory
-   parallelism needs it local. The fix is on branch `perf/threadpool-prototype`:
-
-   ```python
-   export_window = list(export_window)
-   export_window[window_n] = dict(window, start=start)
-   ```
-
-   Only `["end"]` is ever read back by `optimise_export`, so nothing depends on the write being
-   visible. **Port this across.**
-
-2. **`round_py` is poison in any hot loop.** It is `snprintf`+`strtod`, ~96ns, and serialises on
-   libc's global locale — with constant work per thread it degrades **17x at 8 threads**. Two calls
-   remain (`iboost_next`, `car_soc_next`), both once per run, both harmless. Do not add more.
-
-3. **The prediction cache is worth keeping.** ~6,518 of 25,727 calls are hits (44%), saving far more
-   than the hashing costs. Resolve hits in Python before building the batch.
-
-4. **Test isolation.** Any new test that calls `calculate_plan` must not use the shared `my_predbat`
-   fixture — it arrives carrying whatever earlier tests left on it and `calculate_plan` raises. Follow
-   `run_annual_integration_isolated` / `run_window_cache_tests_isolated`: take a fresh instance from
-   `create_predbat()` and load a debug YAML.
-
-5. **`cd` in a Bash tool call does not persist**, and `git checkout` carries uncommitted changes
-   across branches. Both bit during this session.
-
-## Verification protocol
-
-Non-negotiable, in this order:
-
-```bash
-cd coverage && source setup.csh
-
-# 1. byte-identical plans - the real gate
-python3 ../apps/predbat/unit_test.py --random-run \
-    --random-template cases/predbat_debug_agile1.yaml \
-    --random-scenarios cases/random_scenarios.yaml
-python3 ../apps/predbat/unit_test.py --random-compare cases/random_results.json random_results.json
-# require: 20 unchanged, +0.0000 on metric, cost and all three PV futures
-
-# 2. kernel parity (includes the SoC bucket table check)
-./run_all --test kernel_parity
-
-# 3. full suite including slow tests
-./run_all
-
-# 4. quality gates
-./run_pre_commit        # re-run after the dictionary auto-sorts, then re-stage
-```
-
-Benchmark by summing `runtime_s` across the 20 scenarios, best of 3. Run-to-run spread is ~2-6%, so
-do not trust a single run — that mistake was made earlier in this work and produced a bogus 18%
-"win" that turned out to be noise.
-
-## Suggested order
-
-1. Port the `thread_run_prediction_export` mutation fix from `perf/threadpool-prototype`.
-2. Split `thread_run_prediction_*` into `_prepare_*` + shaping. **Verify parity here** — this step
-   alone should be a no-op.
-3. Add `BatchRunner` / `BatchHandle` and switch `launch_*` to enqueue. Keep `n_threads=1`.
-   **Verify parity.** This isolates the marshalling win with no concurrency risk.
-4. Turn on threading (`n_threads` from the `threads` config). **Verify parity again** — results were
-   bit-identical at every thread count in the C-level test, so any divergence here is a Python bug.
-5. Only then remove the process pool, `wrapped_run_prediction_*`, `PRED_GLOBAL` and `DummyThread`.
-
-## Dead ends - do not repeat these
-
-- **Python `ThreadPool` instead of the process pool.** `pk_run` does release the GIL (`ctypes.CDLL`)
-  and the kernel is reentrant, but per-call Python overhead makes the parallel fraction too small.
-  Peaks at **1.15x on two threads** then degrades below serial. Recorded with measurements on branch
-  `perf/threadpool-prototype` (commit message, marked do-not-merge).
-- **The process pool itself.** 1.11x on 16 cores; the parent's per-call pickling is a serial
-  bottleneck. Removing it costs almost nothing.
-- **Expecting the batch call itself to be faster.** `pk_run_batch` vs a loop of `pk_run` over 120
-  scenarios measured **0.99x**. The C boundary crossing was never the cost. The batch is worth doing
-  for the Python-side savings and because it is the only way to use threads — not for the call
-  itself.
-
-## Related work in flight
-
-`PR #4536` (`perf/window-bounds-cache`) caches window start/end arrays and the prediction-cache key
-tuple, worth **-21%** on its own and independent of this branch. It should compose: it removes Python
-per-call work, this removes C++ per-call work. If both land, re-profile before assuming what is left.
-
-Note that batching **partly subsumes** the window-bounds cache, since a batch ships window bounds once
-rather than per call. The `sim_hash` half still matters.
diff --git a/apps/predbat/hass.py b/apps/predbat/hass.py
index cf4044058..58d48479f 100644
--- a/apps/predbat/hass.py
+++ b/apps/predbat/hass.py
@@ -164,9 +164,7 @@ def log(self, msg, quiet=True):
         log_size = self.logfile.tell()
         if log_size > 10000000 and threading.current_thread() is threading.main_thread():
             # Only rotate from the main thread to avoid race conditions with
-            # component threads that also call log() — a Threading.Lock cannot
-            # be used here because the logfile object must survive pickle for
-            # the multiprocessing pool.
+            # component threads that also call log().
             for num_logs in range(max_logs - 1, 0, -1):
                 filename = "predbat." + format(num_logs) + ".log"
                 if os.path.isfile(filename):
diff --git a/apps/predbat/load_ml_component.py b/apps/predbat/load_ml_component.py
index 8b5425b16..5d58c2546 100644
--- a/apps/predbat/load_ml_component.py
+++ b/apps/predbat/load_ml_component.py
@@ -736,8 +736,10 @@ async def run(self, seconds, first):
             self.log("ML Component: No training needed, model age is {} hours".format(dp2(retrain_age_seconds / 3600.0)))
 
         if should_train or should_fetch:
-            # Set load_ml_calculating across all NumPy-heavy work (training + predict + save)
-            # so that the plan's multiprocessing pool is never fork()ed while Numpy threads are active.
+            # Set load_ml_calculating across all NumPy-heavy work (training + predict + save). This used
+            # to gate the plan's multiprocessing pool off while NumPy threads were active (fork() is
+            # unsafe with live threads); the pool is gone, so this is now purely a status flag surfaced
+            # via is_calculating() and logged by calculate_plan().
             if self.base.prediction_started:
                 self.log("ML Component: Waiting for current prediction cycle to complete before running ML work")
                 while self.base.prediction_started:
diff --git a/apps/predbat/marginal.py b/apps/predbat/marginal.py
index b4a49c8b8..e1f6635dc 100644
--- a/apps/predbat/marginal.py
+++ b/apps/predbat/marginal.py
@@ -86,8 +86,7 @@ def calculate_marginal_costs(self):
                     if minute in modified_load:
                         modified_load[minute] += extra_per_step
 
-                # Create a fresh Prediction with the modified load; this updates PRED_GLOBAL
-                # which is safe since we run synchronously (pool is idle at this point)
+                # Create a fresh Prediction with the modified load.
                 # No need to include 10% extra load as we only run normal simulations.
                 pred = Prediction(self, self.pv_forecast_minute_step, self.pv_forecast_minute_step, modified_load, modified_load)
 
diff --git a/apps/predbat/plan.py b/apps/predbat/plan.py
index c0fff625c..c74cc0fb9 100644
--- a/apps/predbat/plan.py
+++ b/apps/predbat/plan.py
@@ -18,7 +18,6 @@
 """
 
 import copy
-import traceback
 from datetime import datetime, timedelta
 from multiprocessing import cpu_count
 from const import PREDICT_STEP, PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10, PV_SCENARIO_PV90, TIME_FORMAT, MINUTE_WATT
@@ -64,26 +63,6 @@ def scenario_hash_entry(kind, window_n, value):
     return acc ^ (acc >> 31)
 
 
-"""
-Used to mimic threads when they are disabled
-"""
-
-
-class DummyThread:
-    def __init__(self, result):
-        """
-        Store the data into the class
-        """
-        self.result = result
-        time.sleep(0)  # Yield control
-
-    def get(self):
-        """
-        Return the result
-        """
-        return self.result
-
-
 class Plan:
     """Plan optimisation mixin for finding optimal charge/discharge windows.
 
@@ -1576,16 +1555,6 @@ def calculate_plan(self, recompute=True, debug_mode=False, publish=True):
                     self.log(line)
                 self.publish_html_plan(pv_forecast_minute_step, pv_forecast_minute10_step, load_minutes_step, load_minutes_step10, self.end_record)
 
-        # Destroy pool
-        if self.pool:
-            try:
-                self.pool.close()
-                self.pool.join()
-            except Exception as e:
-                self.log("Warn: failed to close thread pool: {}".format(e))
-                self.log("Warn: " + traceback.format_exc())
-            self.pool = None
-
         # Record planning duration for SLO metrics
         self.plan_last_duration_seconds = time.time() - plan_start_time
         metrics().planning_duration_seconds.observe(self.plan_last_duration_seconds)
diff --git a/apps/predbat/predbat.py b/apps/predbat/predbat.py
index 59f3485f9..c097a7463 100644
--- a/apps/predbat/predbat.py
+++ b/apps/predbat/predbat.py
@@ -68,7 +68,6 @@
     INVERTER_QUICK_UPDATE_SECONDS,
 )
 from config import APPS_SCHEMA, CONFIG_ITEMS
-from prediction import reset_prediction_globals
 from utils import minutes_since_yesterday, dp1, dp2, dp3
 from predheat import PredHeat
 from octopus import Octopus
@@ -271,27 +270,10 @@ def time_abs_str(self, minute):
         """
         return (self.midnight + timedelta(minutes=minute)).strftime("%m-%d %H:%M:%S")
 
-    def cleanup_pool(self):
-        """
-        Terminate and clean up the multiprocessing pool if it is active.
-
-        Ensures worker processes are properly terminated to prevent orphaned
-        processes when the prediction loop exits unexpectedly.
-        """
-        if getattr(self, "pool", None):
-            try:
-                self.pool.terminate()
-                self.pool.join()
-            except Exception as e:
-                self.log("Warn: Failed to terminate multiprocessing pool: {}".format(e))
-                self.log(traceback.format_exc())
-            self.pool = None
-
     def reset(self):
         """
         Init stub
         """
-        reset_prediction_globals()
         self.text_plan = "Computing please wait..."
         self.prediction_cache_enable = True
         self.base_load = 0
@@ -316,8 +298,6 @@ def reset(self):
 
         # Forecast.solar API request metrics for monitoring
         self.currency_symbols = self.args.get("currency_symbols", "£p")
-        self.cleanup_pool()
-        self.pool = None
         self.watch_list = []
         self.restart_active = False
         self.inverter_needs_reset = False
@@ -1177,15 +1157,6 @@ def download_predbat_version(self, version):
             # Kill the current threads
             self.log("Kill current threads before update")
             self.stop_thread = True
-            if self.pool:
-                self.log("Warn: Killing current threads before update...")
-                try:
-                    self.pool.close()
-                    self.pool.join()
-                except Exception as e:
-                    self.log("Warn: Failed to close thread pool: {}".format(e))
-                    self.log("Warn: " + traceback.format_exc())
-                self.pool = None
 
             # Perform the update
             self.log("Perform the update.....")
@@ -1609,7 +1580,6 @@ def initialize(self):
         # "Predbat defaulted it" or "another component already overwrote it" (issue #4494 follow-up).
         self.args_from_apps_yaml = copy.deepcopy(self.args)
         self.apps_yaml_override_warned = set()  # {arg} already warned about via set_arg_auto()
-        self.pool = None
         self.log("Predbat: Startup {}".format(__name__))
         self.update_time(print=False)
         self.started_time = self.now_utc_real
@@ -1731,16 +1701,6 @@ async def terminate(self):
         self.log("Info: Components stopped")
 
         await asyncio.sleep(0)
-        if hasattr(self, "pool"):
-            if self.pool:
-                self.log("Info: Terminating thread pool...")
-                try:
-                    self.pool.close()
-                    self.pool.join()
-                except Exception as e:
-                    self.log("Warn: Failed to close thread pool {}".format(e))
-                    self.log("Warn: " + traceback.format_exc())
-                self.pool = None
         self.log("Predbat terminated")
 
     def update_time_loop(self, cb_args):
@@ -1773,7 +1733,6 @@ def update_time_loop(self, cb_args):
                 # Always clear the active flag, even on early return or exception, so the
                 # web spinner and predbat.active switch don't get stuck on
                 self.expose_config("active", False)
-                self.cleanup_pool()
         elif not self.prediction_started:
             time_now = datetime.now()
             inverter_data_last_fetch = self.inverter_data_last_fetch
@@ -1855,7 +1814,6 @@ def run_time_loop(self, cb_args):
                 # Always clear the active flag, even on early return or exception, so the
                 # web spinner and predbat.active switch don't get stuck on
                 self.expose_config("active", False)
-                self.cleanup_pool()
 
     def run_time_loop_balance(self, cb_args):
         """
diff --git a/apps/predbat/prediction.py b/apps/predbat/prediction.py
index 3d48daf4e..7306b7802 100644
--- a/apps/predbat/prediction.py
+++ b/apps/predbat/prediction.py
@@ -25,48 +25,6 @@
 from prediction_kernel import create_kernel_context, kernel_supported, run_prediction_kernel
 
 
-# Only assign globals once to avoid re-creating them with processes are forked
-if not "PRED_GLOBAL" in globals():
-    PRED_GLOBAL = {}
-
-
-def reset_prediction_globals():
-    global PRED_GLOBAL
-    PRED_GLOBAL = {}
-
-
-def wrapped_run_prediction_single(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step):
-    """Reconstruct a Prediction from the global shared state and run a single-scenario prediction in a worker process."""
-    global PRED_GLOBAL
-    pred = Prediction()
-    pred.__dict__ = PRED_GLOBAL["dict"].copy()
-    return pred.thread_run_prediction_single(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step)
-
-
-def wrapped_run_prediction_charge(try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-    """Reconstruct a Prediction from the global shared state and run a charge-window trial prediction in a worker process."""
-    global PRED_GLOBAL
-    pred = Prediction()
-    pred.__dict__ = PRED_GLOBAL["dict"].copy()
-    return pred.thread_run_prediction_charge(try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record)
-
-
-def wrapped_run_prediction_charge_min_max(try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-    """Reconstruct a Prediction from the global shared state and run a charge min/max trial prediction in a worker process."""
-    global PRED_GLOBAL
-    pred = Prediction()
-    pred.__dict__ = PRED_GLOBAL["dict"].copy()
-    return pred.thread_run_prediction_charge_min_max(try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record)
-
-
-def wrapped_run_prediction_export(this_export_limit, start, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-    """Reconstruct a Prediction from the global shared state and run an export-window trial prediction in a worker process."""
-    global PRED_GLOBAL
-    pred = Prediction()
-    pred.__dict__ = PRED_GLOBAL["dict"].copy()
-    return pred.thread_run_prediction_export(this_export_limit, start, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record)
-
-
 def get_diff(battery_draw, pv_dc, pv_ac, load_yesterday, inverter_loss, inverter_loss_recp):
     """
     Get AC output difference
@@ -105,7 +63,6 @@ def __init__(self, base=None, pv_forecast_minute_step=None, pv_forecast_minute10
         pv_forecast_minute90_step and load_minutes_step90 fall back to the nominal step arrays when None, so
         every existing call site that never requests the pv90 scenario keeps working unchanged.
         """
-        global PRED_GLOBAL
         if base:
             self.minutes_now = base.minutes_now
             self.log = base.log
@@ -216,11 +173,6 @@ def __init__(self, base=None, pv_forecast_minute_step=None, pv_forecast_minute10
             if self.prediction_kernel_enable:
                 self.kernel_handle = create_kernel_context(self)
 
-            # Store this dictionary in global so we can reconstruct it in the thread without passing the data
-            PRED_GLOBAL["dict"] = self.__dict__.copy()
-
-            # Deliberately after the PRED_GLOBAL snapshot: a pool worker reconstructs its Prediction
-            # from that dict and never queues anything, so it has no business carrying a batch.
             self.pending_batch = []
             self.batch_threads = 1
 
diff --git a/apps/predbat/tests/test_download.py b/apps/predbat/tests/test_download.py
index 5ba19ef1b..b2372e7e2 100644
--- a/apps/predbat/tests/test_download.py
+++ b/apps/predbat/tests/test_download.py
@@ -1693,7 +1693,6 @@ def _test_predbat_download_main_uses_configured_repository(my_predbat):
     class DummyPredBat:
         def __init__(self):
             self.stop_thread = False
-            self.pool = None
 
         def log(self, *_args, **_kwargs):
             return None
@@ -1725,7 +1724,6 @@ def _test_predbat_download_tag_uses_default_repository(my_predbat):
     class DummyPredBat:
         def __init__(self):
             self.stop_thread = False
-            self.pool = None
 
         def log(self, *_args, **_kwargs):
             return None
diff --git a/apps/predbat/tests/test_export_commitment.py b/apps/predbat/tests/test_export_commitment.py
index fdd046ac7..c7ca43d0e 100644
--- a/apps/predbat/tests/test_export_commitment.py
+++ b/apps/predbat/tests/test_export_commitment.py
@@ -23,7 +23,6 @@ def setup_single_export_window(my_predbat, rate_import=10.0, rate_export=30.0, b
     my_predbat.load_user_config()
     my_predbat.fetch_config_options()
     reset_inverter(my_predbat)
-    my_predbat.pool = None
 
     my_predbat.forecast_minutes = 24 * 60
     end_record = my_predbat.forecast_minutes
diff --git a/apps/predbat/tests/test_infra.py b/apps/predbat/tests/test_infra.py
index 0c8b3220a..0a8160017 100644
--- a/apps/predbat/tests/test_infra.py
+++ b/apps/predbat/tests/test_infra.py
@@ -9,7 +9,7 @@
 # pylint: disable=attribute-defined-outside-init
 
 from datetime import datetime, timedelta
-from prediction import wrapped_run_prediction_single, Prediction
+from prediction import Prediction
 from matplotlib import pyplot as plt
 import asyncio
 import numpy as np
@@ -805,7 +805,7 @@ def simple_scenario(
             metric_keep,
             final_iboost,
             final_carbon_g,
-        ) = wrapped_run_prediction_single(charge_limit_best, charge_window_best, export_window_best, export_limit_best, pv10, end_record=(my_predbat.end_record), step=5)
+        ) = prediction.thread_run_prediction_single(charge_limit_best, charge_window_best, export_window_best, export_limit_best, pv10, end_record=(my_predbat.end_record), step=5)
     else:
         (
             metric,
diff --git a/apps/predbat/tests/test_pv90.py b/apps/predbat/tests/test_pv90.py
index bf61b75e3..3844e2dc9 100644
--- a/apps/predbat/tests/test_pv90.py
+++ b/apps/predbat/tests/test_pv90.py
@@ -10,7 +10,6 @@
 """Tests for the pv90 upside forecast scenario."""
 
 from const import PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10, PV_SCENARIO_PV90
-from plan import DummyThread
 from tests.test_infra import reset_inverter, reset_rates
 
 
@@ -785,8 +784,8 @@ def _setup_calculate_plan_with_real_windows(my_predbat):
     window in each direction to search, so the counting assertions below are actually exercised.
 
     threads is forced to 0 - the same idiom already used by test_execute.py, test_random_scenarios.py and
-    test_single_debug.py - so calculate_plan() takes the synchronous DummyThread fallback instead of
-    spinning up a real multiprocessing Pool, keeping the test fast and deterministic.
+    test_single_debug.py - so calculate_plan() sizes the kernel's batch thread pool to a single thread
+    rather than the 'auto' cpu_count() default, keeping the test fast and deterministic.
 
     Returns a snapshot dict for _restore_calculate_plan_with_real_windows.
     """
@@ -1103,6 +1102,22 @@ def counting(loop_soc, window_n, charge_limit, charge_window, export_window, exp
     return failed
 
 
+class FakeHandle:
+    """Minimal result-handle double with a get(), standing in for a real BatchHandle.
+
+    Used to substitute an encoded result into a launch_run_prediction_* return value without routing
+    it back through the kernel/batch machinery.
+    """
+
+    def __init__(self, result):
+        """Store the pre-computed result tuple."""
+        self.result = result
+
+    def get(self):
+        """Return the stored result."""
+        return self.result
+
+
 def test_pv90_charge_limit_results_paired_with_try_soc(my_predbat):
     """optimise_charge_limit's pv90 results must stay paired with the try_soc that produced them.
 
@@ -1149,7 +1164,7 @@ def encoding_launch(loop_soc, window_n, charge_limit, charge_window, export_wind
         if pv_scenario in encoded_scenarios:
             encoded_result = list(handle.get())
             encoded_result[0] = loop_soc
-            handle = DummyThread(tuple(encoded_result))
+            handle = FakeHandle(tuple(encoded_result))
         return handle
 
     original_launch_min_max = my_predbat.launch_run_prediction_charge_min_max
@@ -1160,7 +1175,7 @@ def encoding_launch_min_max(loop_soc, window_n, charge_limit, charge_window, exp
         if pv_scenario in encoded_scenarios:
             encoded_result = list(handle.get())
             encoded_result[0] = loop_soc
-            handle = DummyThread(tuple(encoded_result))
+            handle = FakeHandle(tuple(encoded_result))
         return handle
 
     original_optimise_charge_limit = my_predbat.optimise_charge_limit
diff --git a/apps/predbat/userinterface.py b/apps/predbat/userinterface.py
index 236de5dce..1cbacc694 100644
--- a/apps/predbat/userinterface.py
+++ b/apps/predbat/userinterface.py
@@ -33,7 +33,6 @@
 from predbat import THIS_VERSION
 
 DEBUG_EXCLUDE_LIST = [
-    "pool",
     "ha_interface",
     "components",
     "prediction",

From ed15d44fc77bedc1e74252147977434532714192 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 21:24:12 +0100
Subject: [PATCH 16/24] fix(prediction): drain a pending batch before a saving
 run, and pin result routing

Three things the batch was relying on nobody getting wrong.

Nothing pinned flush_batch's job->result mapping. Injecting a reversed mapping
(batched[len(kernel_jobs) - 1 - index]) passed the prediction_batch, kernel_parity
and model_kernel suites in full: every trial in a fan-out would silently take
another trial's cost. The hole was that no test compared values across a multi-job
batch - test_batch_is_lazy only checked results were not None, and the threading
test compared the batch against itself at different thread counts, which cannot see
a thread-count-independent Python defect because n_threads goes straight to C.
That test is now test_batch_results_match_their_own_job and uses an external
oracle: ten distinct try_soc values queued in one batch, each handle compared
against thread_run_prediction_charge for the same SoC with the prediction cache
cleared between the two runs. The SoCs are asserted to give ten different costs
first, so a misrouted result cannot match by coincidence - the previous scenario
saturated and gave nine identical costs, hence the trial spread across every charge
window and the different scenario seed. With the reversed mapping applied, all ten
jobs fail at all three thread counts.

A saving run is the only one that populates predict_soc_best and friends. If a
batch were left pending across it, the next handle read would flush it and
reset_kernel_run_state would blank exactly those attributes - the published plan
and the debug HTML would come back empty (verified: 288 entries -> 0 with the guard
removed) - and a job flushed after its inputs were mutated would be cached under a
key hashed from the old ones. All four fan-outs drain their handles first so
neither is reachable, but run_prediction now drains any pending batch when save is
set. The guard is a single test of a local on the ~250k non-save calls per plan.

pending_batch and batch_threads were initialised only inside __init__'s `if base:`
branch, so a Prediction built without a base raised AttributeError on its first
enqueue. Nothing constructs one today - the wrapped_* trampolines that did went
with the process pool - which is what made it worth closing.

Also replaces two kernel_parity assertions that could not fail: the SoC series
check now requires the single-scenario reference to have materialised the series
the batch omits, and car_charging_soc_next (recorded at minute 0, so identical for
every job in a fan-out) is backed by an assertion that the kernel fills it at all.

The four thread_run_prediction_* docstrings no longer claim to run in a thread;
they are the direct synchronous path the batch falls back to. The four
queue_run_prediction_* docstrings now state that inputs are read at flush time and
must not be mutated before get().

20-scenario random-compare: +0.0000 on metric, cost and all three PV futures.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/prediction.py                  |  65 +++++++--
 apps/predbat/tests/test_kernel_parity.py    |  25 +++-
 apps/predbat/tests/test_prediction_batch.py | 141 +++++++++++++++++---
 3 files changed, 196 insertions(+), 35 deletions(-)

diff --git a/apps/predbat/prediction.py b/apps/predbat/prediction.py
index 7306b7802..5b8ea3acb 100644
--- a/apps/predbat/prediction.py
+++ b/apps/predbat/prediction.py
@@ -173,8 +173,10 @@ def __init__(self, base=None, pv_forecast_minute_step=None, pv_forecast_minute10
             if self.prediction_kernel_enable:
                 self.kernel_handle = create_kernel_context(self)
 
-            self.pending_batch = []
-            self.batch_threads = 1
+        # Outside the `if base:` block on purpose: a Prediction built without a base is still a valid
+        # object and its first enqueue_prediction would otherwise raise AttributeError
+        self.pending_batch = []
+        self.batch_threads = 1
 
     def _prepare_single(self, charge_limit, export_limits):
         """Copy the caller's limit lists for a single-scenario trial - shared by thread_run_prediction_single and the batch path.
@@ -196,8 +198,11 @@ def _prepare_charge(self, try_soc, window_n, charge_limit, all_n):
         return try_charge_limit
 
     def thread_run_prediction_single(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step):
-        """
-        Run single prediction in a thread
+        """Run one single-scenario prediction now and return its result.
+
+        Nothing runs in a Python thread any more: this is the direct synchronous path, kept as the
+        reference the batch is checked against and as what a queued job falls back to when the kernel
+        will not take it.
         """
         charge_limit, export_limits = self._prepare_single(charge_limit, export_limits)
 
@@ -223,13 +228,20 @@ def thread_run_prediction_single(self, charge_limit, charge_window, export_windo
         return (cost, import_kwh_battery, import_kwh_house, export_kwh, soc_min, soc, soc_min_minute, battery_cycle, metric_keep, final_iboost, final_carbon_g)
 
     def queue_run_prediction_single(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step):
-        """Queue a single-scenario prediction, returning a handle - the batch runs on the first get()"""
+        """Queue a single-scenario prediction, returning a handle - the batch runs on the first get().
+
+        The window lists and dicts are read at flush time, not now, so the caller must not mutate
+        anything it passed in before calling get() on the returned handle.
+        """
         charge_limit, export_limits = self._prepare_single(charge_limit, export_limits)
         return self.enqueue_prediction(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step, self.prediction_cache_enable)
 
     def thread_run_prediction_charge(self, try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """
-        Run prediction in a thread
+        """Run one charge-window trial prediction now and return its result.
+
+        Nothing runs in a Python thread any more: this is the direct synchronous path, kept as the
+        reference the batch is checked against and as what a queued job falls back to when the kernel
+        will not take it.
         """
 
         try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
@@ -268,7 +280,11 @@ def thread_run_prediction_charge(self, try_soc, window_n, charge_limit, charge_w
         )
 
     def queue_run_prediction_charge(self, try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """Queue a charge-window trial prediction, returning a handle"""
+        """Queue a charge-window trial prediction, returning a handle.
+
+        The window lists and dicts are read at flush time, not now, so the caller must not mutate
+        anything it passed in before calling get() on the returned handle.
+        """
         try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
         return self.enqueue_prediction(try_charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, PREDICT_STEP, self.prediction_cache_enable)
 
@@ -292,8 +308,11 @@ def scan_soc_range(self, predict_soc, window):
         return min_soc, max_soc
 
     def thread_run_prediction_charge_min_max(self, try_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """
-        Run prediction in a thread
+        """Run one charge-window trial prediction now and return its result plus the SoC range.
+
+        Nothing runs in a Python thread any more: this is the direct synchronous path, kept as the
+        reference the batch is checked against and as what a queued job falls back to when the kernel
+        will not take it.
         """
 
         try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
@@ -343,6 +362,9 @@ def queue_run_prediction_charge_min_max(self, try_soc, window_n, charge_limit, c
 
         Uncached, exactly as the direct path is: the SoC range is not part of the cached result, so a
         hit would answer with the wrong shape.
+
+        The window lists and dicts are read at flush time, not now, so the caller must not mutate
+        anything it passed in before calling get() on the returned handle.
         """
         try_charge_limit = self._prepare_charge(try_soc, window_n, charge_limit, all_n)
         range_window = None if all_n else charge_window[window_n]
@@ -373,8 +395,11 @@ def _prepare_export(self, this_export_limit, start, window_n, export_window, exp
         return export_window, export_limits
 
     def thread_run_prediction_export(self, this_export_limit, start, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """
-        Run prediction in a thread
+        """Run one export-window trial prediction now and return its result.
+
+        Nothing runs in a Python thread any more: this is the direct synchronous path, kept as the
+        reference the batch is checked against and as what a queued job falls back to when the kernel
+        will not take it.
         """
         export_window, export_limits = self._prepare_export(this_export_limit, start, window_n, export_window, export_limits, all_n)
 
@@ -400,7 +425,11 @@ def thread_run_prediction_export(self, this_export_limit, start, window_n, charg
         return metricmid, import_kwh_battery, import_kwh_house, export_kwh, soc_min, soc, soc_min_minute, battery_cycle, metric_keep, final_iboost, final_carbon_g
 
     def queue_run_prediction_export(self, this_export_limit, start, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """Queue an export-window trial prediction, returning a handle"""
+        """Queue an export-window trial prediction, returning a handle.
+
+        The window lists and dicts are read at flush time, not now, so the caller must not mutate
+        anything it passed in before calling get() on the returned handle.
+        """
         export_window, export_limits = self._prepare_export(this_export_limit, start, window_n, export_window, export_limits, all_n)
         return self.enqueue_prediction(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, PREDICT_STEP, self.prediction_cache_enable)
 
@@ -427,6 +456,16 @@ def run_prediction(self, charge_limit, charge_window, export_window, export_limi
         KERNEL_PARITY_REVISION (prediction_kernel.py) and PK_PARITY_REVISION (prediction_kernel.cpp)
         must both be bumped, and the kernel_parity test must pass (cd coverage && ./run_all --test kernel_parity).
         """
+        # A saving run publishes predict_soc_best and friends, and it is the only run that does. If a
+        # batch were left pending, the next handle read would flush it and reset_kernel_run_state
+        # would blank exactly those attributes, emptying the published plan and the debug HTML; a job
+        # flushed after its inputs were mutated would also be cached under a key hashed from the old
+        # ones, poisoning the shared cache. Draining first makes both impossible. Every fan-out site
+        # already drains its handles before saving, so this is the class defending its own invariant
+        # rather than a live fix - and it costs the ~250k non-save calls a single test of a local.
+        if save and self.pending_batch:
+            self.flush_batch()
+
         # The cache key is only wanted when the cache is actually in play - a saving run always
         # simulates - so it is not computed otherwise. Building it as one tuple hash keeps the
         # per-window hashing in C rather than looping in Python, which matters because this runs on
diff --git a/apps/predbat/tests/test_kernel_parity.py b/apps/predbat/tests/test_kernel_parity.py
index 919b31fd0..3183e6cdf 100644
--- a/apps/predbat/tests/test_kernel_parity.py
+++ b/apps/predbat/tests/test_kernel_parity.py
@@ -950,10 +950,17 @@ def check_batch_results(jobs, reference, batched, n_threads):
             if result_tuple[field] != single[field]:
                 print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
                 failed = True
-        # The batch never materialises the SoC series - that is what makes it affordable
-        if result_tuple[11] != {}:
-            print("ERROR: job {} returned a SoC series from the batch path".format(index))
+        # The batch never materialises the SoC series - that is what makes it affordable. Checked
+        # against the single path rather than against an empty dict on its own, so this fails both
+        # if the batch starts filling the series and if the reference stopped filling it (which
+        # would make the batch's emptiness prove nothing).
+        if result_tuple[11] or not single[11]:
+            print("ERROR: job {} SoC series: batch has {} entries, single reference has {}".format(index, len(result_tuple[11]), len(single[11])))
             failed = True
+        # car_charging_soc_next is recorded at minute 0, before any trial limit can move it, so it is
+        # the same value for every job in the fan-out - comparing it job by job pins that the batch
+        # assembles it like the single path does, but cannot catch a mis-routed result. That the
+        # kernel fills it at all is asserted once per run in run_batch_parity_tests.
         for field, name in [(12, "car_charging_soc_next"), (13, "iboost_next"), (14, "iboost_running"), (15, "iboost_running_solar"), (16, "iboost_running_full")]:
             if result_tuple[field] != single[field]:
                 print("ERROR: job {} at {} threads differs on {}: batch {} single {}".format(index, n_threads, name, result_tuple[field], single[field]))
@@ -988,6 +995,18 @@ def run_batch_parity_tests(my_predbat, count=60):
         return True
 
     failed = False
+    # The per-job car_charging_soc_next comparison in check_batch_results is only worth anything if
+    # the kernel actually fills that field - otherwise both sides are the Prediction's own baseline
+    # copied through, and the comparison is two copies of the same list. Seed 4321 gives this
+    # scenario cars; assert it rather than assume it, so changing the seed cannot silently hollow the
+    # comparison out.
+    if not prediction.num_cars:
+        print("ERROR: batch parity scenario has no cars - the car_charging_soc_next comparison is vacuous")
+        failed = True
+    elif reference[0][0][12][: prediction.num_cars] == prediction.car_charging_soc_next[: prediction.num_cars]:
+        print("ERROR: car_charging_soc_next was not filled by the kernel, it is the Prediction's baseline {}".format(prediction.car_charging_soc_next))
+        failed = True
+
     for n_threads in (1, 2, 4, 8):
         batched = prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads)
         if batched is None:
diff --git a/apps/predbat/tests/test_prediction_batch.py b/apps/predbat/tests/test_prediction_batch.py
index 9198e879d..36bcb3184 100644
--- a/apps/predbat/tests/test_prediction_batch.py
+++ b/apps/predbat/tests/test_prediction_batch.py
@@ -14,8 +14,9 @@
 that deferral relies on: that a trial owns its inputs rather than sharing the caller's, that a queued
 job returns exactly what the direct thread_run_prediction_* path returns for every job shape and step,
 that nothing runs before the first get() and one get() drains the batch, that cache hits and
-intra-batch duplicates never reach the kernel while min/max jobs are never cached at all, and that
-every job the kernel will not take still runs on the engine the direct path would have used.
+intra-batch duplicates never reach the kernel while min/max jobs are never cached at all, that every
+job the kernel will not take still runs on the engine the direct path would have used, and that every
+handle in a multi-job batch comes back with its own job's result rather than a neighbour's.
 """
 
 
@@ -81,6 +82,38 @@ def test_export_trial_does_not_mutate_caller_window(my_predbat):
     return failed
 
 
+def test_batch_state_exists_without_a_base(my_predbat):
+    """A Prediction built without a base must still be able to queue, returns True on failure.
+
+    pending_batch and batch_threads used to be initialised only inside __init__'s `if base:` branch,
+    so a baseless Prediction raised AttributeError on its first enqueue. Nothing constructs one today
+    - the wrapped_* trampolines that did went with the process pool - which is exactly why the trap
+    is easy to walk back into.
+    """
+    print("**** Running baseless Prediction tests ****")
+    failed = False
+    prediction = Prediction()
+
+    if prediction.pending_batch != []:
+        print("ERROR: a baseless Prediction has pending_batch {}".format(prediction.pending_batch))
+        failed = True
+    if prediction.batch_threads != 1:
+        print("ERROR: a baseless Prediction has batch_threads {}".format(prediction.batch_threads))
+        failed = True
+
+    handle = prediction.enqueue_prediction([], [], [], [], PV_SCENARIO_NOMINAL, 0, PREDICT_STEP, False)
+    if len(prediction.pending_batch) != 1:
+        print("ERROR: enqueue on a baseless Prediction left {} pending job(s)".format(len(prediction.pending_batch)))
+        failed = True
+    if handle.job.result is not None:
+        print("ERROR: enqueue on a baseless Prediction ran the job")
+        failed = True
+
+    if not failed:
+        print("Baseless Prediction tests passed")
+    return failed
+
+
 def make_batch_prediction(my_predbat, seed=7):
     """Build a kernel-enabled Prediction plus a charge/export window layout to fan out over"""
     rng = random.Random(seed)
@@ -377,42 +410,111 @@ def spy_batch(pred, jobs, n_threads):
     return failed
 
 
-def test_batch_threads_do_not_change_results(my_predbat):
-    """A threaded flush must return exactly what a serial one does, returns True on failure.
+def test_save_run_drains_pending_batch(my_predbat):
+    """A saving run must flush any pending batch before it runs, returns True on failure.
 
-    pk_run_batch is pinned bit-identical across thread counts at the C level (kernel_parity), so this
-    is really checking the Python side: that results are matched back to their own job regardless of
-    how the kernel split the work.
+    A save run is the only one that populates predict_soc_best and friends, and it goes down the
+    Python engine because kernel_supported() refuses save. If a batch were left pending across it,
+    the next handle read would flush it and reset_kernel_run_state would blank exactly those
+    attributes - the published plan and the debug HTML would come back empty. All four fan-outs drain
+    their handles before any save run today, so this pins the guard, not a live bug: it is what stops
+    a fifth fan-out from introducing one.
     """
-    print("**** Running batch threading tests ****")
+    print("**** Running save-run batch drain tests ****")
     failed = False
     prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat)
     end_record = my_predbat.forecast_minutes
+
+    handle = prediction.queue_run_prediction_charge(3.0, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record)
+    if handle.job.result is not None:
+        print("ERROR: the queued job ran before the save run")
+        failed = True
+
+    prediction.run_prediction(charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, end_record=end_record, save="best")
+
+    if prediction.pending_batch:
+        print("ERROR: the save run left {} job(s) pending".format(len(prediction.pending_batch)))
+        failed = True
+    if handle.job.result is None:
+        print("ERROR: the save run did not flush the pending job")
+        failed = True
+    if not prediction.predict_soc_best:
+        print("ERROR: the save run published no predict_soc_best, so this test cannot see it being blanked")
+        failed = True
+
+    # The point of the guard: reading the handle afterwards must not wipe what the save run published
+    published = dict(prediction.predict_soc_best)
+    handle.get()
+    if prediction.predict_soc_best != published:
+        print("ERROR: reading a handle after a save run changed predict_soc_best ({} entries, was {})".format(len(prediction.predict_soc_best), len(published)))
+        failed = True
+
+    if not failed:
+        print("Save-run batch drain tests passed")
+    return failed
+
+
+def test_batch_results_match_their_own_job(my_predbat):
+    """Every handle in a multi-job batch must get its own job's result, at any thread count, returns True on failure.
+
+    The oracle is external, and it has to be: flush_batch resolves cache hits and intra-batch
+    duplicates before the kernel call and then walks the surviving jobs alongside the kernel's
+    results, so an off-by-one or reversed job->result mapping is easy to introduce and returns a
+    perfectly plausible cost for every trial. Comparing a batch against another batch cannot see
+    that - both would be wrong the same way - and neither can sweeping n_threads, which is forwarded
+    verbatim to C with nothing on the Python side varying with it.
+
+    So each handle is compared against thread_run_prediction_charge for the same try_soc, with the
+    prediction cache cleared between the two runs so neither can be answered out of what the other
+    stored. The SoCs are checked to give genuinely distinct costs first, otherwise a misrouted result
+    could match by coincidence.
+
+    The thread sweep rides along: pk_run_batch is pinned bit-identical across thread counts at the C
+    level (kernel_parity), so this pins that the Python side routes results the same way however the
+    kernel split the work.
+    """
+    print("**** Running batch result routing tests ****")
+    failed = False
+    # Seed 5 rather than the shared default: this test needs ten trials with ten different costs, and
+    # the default scenario's battery saturates so that nine of the ten come out identical
+    prediction, charge_window, export_window, charge_limit, export_limits = make_batch_prediction(my_predbat, seed=5)
+    end_record = my_predbat.forecast_minutes
     socs = [round(my_predbat.soc_max * fraction, 2) for fraction in (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0)]
+    # Every charge window takes the trial SoC, not just one: moving a single window's limit saturates
+    # once that window can no longer absorb more, which collapses the costs back together
+    all_n = list(range(len(charge_window)))
+
+    # The oracle: one direct run per SoC, each from a cold cache so it is genuinely simulated
+    expected = []
+    for soc in socs:
+        prediction.prediction_cache = {}
+        expected.append(prediction.thread_run_prediction_charge(soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, all_n, end_record))
+
+    costs = [result[0] for result in expected]
+    if len(set(costs)) != len(costs):
+        print("ERROR: the trial SoCs do not all give distinct costs ({}) - a misrouted result could match by luck".format(costs))
+        failed = True
 
-    reference = None
     for n_threads in (1, 2, 8):
         prediction.batch_threads = n_threads
         prediction.prediction_cache = {}
-        handles = [prediction.queue_run_prediction_charge(soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, None, end_record) for soc in socs]
+        handles = [prediction.queue_run_prediction_charge(soc, 1, charge_limit, charge_window, export_window, export_limits, PV_SCENARIO_NOMINAL, all_n, end_record) for soc in socs]
         results = [handle.get() for handle in handles]
-        if reference is None:
-            reference = results
-        elif results != reference:
-            for index, (got, want) in enumerate(zip(results, reference)):
-                if got != want:
-                    print("ERROR: job {} at {} threads returned {} expected {}".format(index, n_threads, got, want))
-            failed = True
+        for index, (got, want) in enumerate(zip(results, expected)):
+            if got != want:
+                print("ERROR: job {} (try_soc {}) at {} threads returned {} expected {}".format(index, socs[index], n_threads, got, want))
+                failed = True
     prediction.batch_threads = 1
 
     if not failed:
-        print("Batch threading tests passed - identical results at 1, 2 and 8 threads")
+        print("Batch result routing tests passed - {} jobs matched their own direct run at 1, 2 and 8 threads".format(len(socs)))
     return failed
 
 
 def run_prediction_batch_tests(my_predbat):
     """Run every batched prediction test, returns True on failure"""
     failed = test_export_trial_does_not_mutate_caller_window(my_predbat)
+    failed |= test_batch_state_exists_without_a_base(my_predbat)
 
     available, required_failure = kernel_available()
     if not available:
@@ -426,7 +528,8 @@ def run_prediction_batch_tests(my_predbat):
         failed |= test_batch_is_lazy(my_predbat)
         failed |= test_batch_cache_and_dedup(my_predbat)
         failed |= test_batch_fallbacks(my_predbat)
-        failed |= test_batch_threads_do_not_change_results(my_predbat)
+        failed |= test_save_run_drains_pending_batch(my_predbat)
+        failed |= test_batch_results_match_their_own_job(my_predbat)
     finally:
         restore_scenario_state(my_predbat, state)
         my_predbat.prediction_kernel_enable = False

From cee8bdd99e5205293e29de1e190fe607ded8ddbf Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 21:24:27 +0100
Subject: [PATCH 17/24] docs: describe the batch model everywhere the pool used
 to be

The process pool is gone, but several places still document it as how predbat
works - and one of them is read by AI coding assistants as ground truth.

.github/copilot-instructions.md documented self.pool, wrapped_run_prediction_*,
PRED_GLOBAL and DummyThread as current architecture, with code samples an
assistant would happily regenerate. Replaced with the batch model: queue through
launch_run_prediction_*, first get() flushes the fan-out through one pk_run_batch
call, kernel threads rather than worker processes, plus the no-mutation invariant.

double_array()'s docstring justified its from_buffer view with "each pool worker is
a separate process (multiprocessing with fork), so no buffer is ever shared between
workers". That was the stated reason an unsafe-looking construct was safe, so it
needed a real replacement rather than deletion: the views are filled on the calling
thread before the kernel is entered and only ever read afterwards, so even a buffer
deliberately shared between jobs (the window memo) is only read concurrently, and
the one thread that could change it is blocked inside the call. Same staleness
fixed on the module-level loader state and load_kernel's cache note.

plan.py's module and class docstrings still described multi-threaded prediction
runs; the planner is strictly serial Python now and the threading lives inside
pk_run_batch. The four launch_run_prediction_* docstrings now carry the
no-mutation-before-get() invariant, which was documented only in
prediction_batch.py's module docstring - a file a contributor adding a fifth
fan-out would never open.

The window memo comment called window reuse "the bulk of the batching win", true
for the charge fan-outs but not the export one, where _prepare_export builds a
fresh export_window per job and every one misses the memo. BatchJob's docstring
listed step as a field the kernel reads, when run_prediction_kernel_batch
overrides it with PREDICT_STEP and only the Python fallback reads it. BatchHandle
was described as the drop-in for the pool's AsyncResult. The threads doc said '0'
disables threads, when it is clamped up to one kernel thread.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .github/copilot-instructions.md   | 51 +++++++++++++------------------
 apps/predbat/plan.py              | 35 ++++++++++++++++-----
 apps/predbat/prediction_batch.py  |  6 +++-
 apps/predbat/prediction_kernel.py | 29 ++++++++++++------
 docs/apps-yaml.md                 |  2 +-
 5 files changed, 74 insertions(+), 49 deletions(-)

diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index 501f8ac52..9ef7b3fd6 100644
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -88,44 +88,37 @@ The core prediction/planning happens in `Plan` mixin (`apps/predbat/plan.py`) wh
 - `optimise_detailed_pass()` - Fine-tune charge/export windows
 - `run_prediction()` - Execute battery simulation via `Prediction` class
 
-### Thread Pool System
+### Batched Predictions
 
-**Multiprocessing for predictions** (in `apps/predbat/plan.py`):
+There is no process pool and no Python thread pool. Every optimiser fan-out in `plan.py` is
+launch-all-then-collect-all, so the launches queue instead of running and the whole fan-out is
+handed to the C++ kernel in one call, which spreads it across its own threads with the GIL
+released. Python itself is strictly serial.
 
-```python
-from multiprocessing import Pool, cpu_count
-
-# Pool created on demand
-if not self.pool:
-    threads = self.get_arg("threads", "auto")
-    if threads == "auto":
-        self.pool = Pool(processes=cpu_count())
-    else:
-        self.pool = Pool(processes=int(threads))
-```
+**How it works** (`apps/predbat/prediction_batch.py`, mixed into `Prediction`):
 
-**How it works**:
-
-- `self.pool` is a `multiprocessing.Pool` instance (NOT thread pool - uses processes)
-- Pool runs prediction scenarios in parallel using `pool.apply_async()`
-- Wrapped functions in `prediction.py`: `wrapped_run_prediction_single()`, `wrapped_run_prediction_charge()`, etc.
-- Global state shared via `PRED_GLOBAL` dict (copied to child processes)
-- Pool configured via `threads` config option: `"auto"` for CPU count or specific number
-- Returns `DummyThread` objects when pool disabled (for single-threaded testing)
+- `Plan.launch_run_prediction_*()` calls `Prediction.queue_run_prediction_*()`, which builds the
+  trial inputs, appends a `BatchJob` to `self.pending_batch` and returns a `BatchHandle`
+- Nothing has run at that point. The first `BatchHandle.get()` calls `flush_batch()`, which resolves
+  prediction-cache hits and intra-batch duplicates in Python and sends the remainder through one
+  `run_prediction_kernel_batch()` / `pk_run_batch` call; every handle in the fan-out is then resolved
+- A job the kernel will not take (no kernel, stale binary, debug run) falls back to
+  `Prediction.run_prediction()`, which is also the direct path `thread_run_prediction_*()` uses
+- `threads` config option sets `Prediction.batch_threads`, the kernel's thread count. `"auto"` uses
+  the CPU count. Results are identical at any thread count - it only trades CPU for planning time
 
 **Key pattern**:
 
 ```python
-# Async call to run prediction in pool
-if self.pool and self.pool._state == "RUN":
-    han = self.pool.apply_async(wrapped_run_prediction_charge, (args...))
-    return han
-else:
-    # Fallback to synchronous execution
-    result = wrapped_run_prediction_charge(args...)
-    return DummyThread(result)
+# Queue the whole fan-out, then read the handles - the first get() runs all of it
+handles = [self.launch_run_prediction_charge(soc, window_n, ...) for soc in try_socs]
+results = [handle.get() for handle in handles]
 ```
 
+**Invariant**: a caller must not mutate any list or window dict it has passed to
+`launch_run_prediction_*` until it has read the matching handle, because those inputs are not read
+until the batch is flushed.
+
 ### Component System (Critical Pattern)
 
 All major features inherit from `ComponentBase` (see `apps/predbat/component_base.py`):
diff --git a/apps/predbat/plan.py b/apps/predbat/plan.py
index c74cc0fb9..f3efa39f1 100644
--- a/apps/predbat/plan.py
+++ b/apps/predbat/plan.py
@@ -13,8 +13,9 @@
 
 Implements the search algorithm that finds optimal charge and discharge windows
 by exploring combinations of price thresholds, window sizes, and SoC targets.
-Uses multi-threaded prediction runs to evaluate thousands of scenarios and select
-the plan that minimises the overall cost metric.
+The search itself is serial Python: each fan-out queues its scenarios through
+launch_run_prediction_* and the first handle read flushes them all through one
+call to the C++ prediction kernel, which is where the threading now lives.
 """
 
 import copy
@@ -67,8 +68,10 @@ class Plan:
     """Plan optimisation mixin for finding optimal charge/discharge windows.
 
     Implements the search algorithm that explores price thresholds,
-    window combinations, and SoC targets using multi-threaded prediction
-    runs to minimise the overall cost metric.
+    window combinations, and SoC targets to minimise the overall cost
+    metric. Scenarios are evaluated in batches: launch_run_prediction_*
+    queues them and reading the first handle runs the whole batch through
+    one C++ kernel call, which spreads it across its own threads.
     """
 
     def dynamic_load(self):
@@ -648,19 +651,35 @@ def optimise_charge_limit_price_threads(
         )
 
     def launch_run_prediction_single(self, charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step=PREDICT_STEP):
-        """Queue a prediction and return a handle to its result"""
+        """Queue a prediction and return a handle to its result.
+
+        Nothing runs here: the inputs are read when the batch is flushed, so no list or window dict
+        passed in may be mutated until the returned handle's get() has been called.
+        """
         return self.prediction.queue_run_prediction_single(charge_limit, charge_window, export_window, export_limits, pv_scenario, end_record, step)
 
     def launch_run_prediction_charge(self, loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """Queue a prediction and return a handle to its result"""
+        """Queue a prediction and return a handle to its result.
+
+        Nothing runs here: the inputs are read when the batch is flushed, so no list or window dict
+        passed in may be mutated until the returned handle's get() has been called.
+        """
         return self.prediction.queue_run_prediction_charge(loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record)
 
     def launch_run_prediction_charge_min_max(self, loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record):
-        """Queue a prediction and return a handle to its result"""
+        """Queue a prediction and return a handle to its result.
+
+        Nothing runs here: the inputs are read when the batch is flushed, so no list or window dict
+        passed in may be mutated until the returned handle's get() has been called.
+        """
         return self.prediction.queue_run_prediction_charge_min_max(loop_soc, window_n, charge_limit, charge_window, export_window, export_limits, pv_scenario, all_n, end_record)
 
     def launch_run_prediction_export(self, this_export_limit, start, window_n, try_charge_limit, charge_window, try_export_window, try_export, pv_scenario, all_n, end_record):
-        """Queue a prediction and return a handle to its result"""
+        """Queue a prediction and return a handle to its result.
+
+        Nothing runs here: the inputs are read when the batch is flushed, so no list or window dict
+        passed in may be mutated until the returned handle's get() has been called.
+        """
         return self.prediction.queue_run_prediction_export(this_export_limit, start, window_n, try_charge_limit, charge_window, try_export_window, try_export, pv_scenario, all_n, end_record)
 
     def scenario_summary_title(self, record_time):
diff --git a/apps/predbat/prediction_batch.py b/apps/predbat/prediction_batch.py
index 7694a4e39..7070ba641 100644
--- a/apps/predbat/prediction_batch.py
+++ b/apps/predbat/prediction_batch.py
@@ -62,7 +62,11 @@ def shape_batch_result(job, result, soc_range_min, soc_range_max):
 
 
 class BatchHandle:
-    """Result handle for a queued prediction - the drop-in for the pool's AsyncResult"""
+    """Result handle for a queued prediction: get() runs the batch if it has not run yet.
+
+    Deliberately shaped like a future so a fan-out reads as launch-all-then-get-all, but nothing is
+    running in the background - the work happens inside the first get().
+    """
 
     __slots__ = ("pred", "job")
 
diff --git a/apps/predbat/prediction_kernel.py b/apps/predbat/prediction_kernel.py
index 5ecc7755e..1d265d2ed 100644
--- a/apps/predbat/prediction_kernel.py
+++ b/apps/predbat/prediction_kernel.py
@@ -37,7 +37,7 @@
 # Maximum number of cars supported by the kernel (PK_MAX_CARS in prediction_kernel.cpp)
 KERNEL_MAX_CARS = 4
 
-# Module-level loader state, inherited by forked worker processes
+# Module-level loader state - the library is loaded once per process and shared by every Prediction
 KERNEL_LIB = None
 KERNEL_LOAD_TRIED = False
 KERNEL_STATUS = "not loaded"
@@ -215,9 +215,12 @@ class PkBatchResult(ctypes.Structure):
 class BatchJob:
     """One queued prediction: its trial inputs, how the result should be shaped, and the result itself.
 
-    run_prediction_kernel_batch reads only the input fields and the SoC range steps. sim_hash,
+    run_prediction_kernel_batch reads only the input fields (charge_limit, charge_window,
+    export_window, export_limits, pv_scenario, end_record) and the SoC range steps. step, sim_hash,
     want_range, range_window and result belong to the batch runner in prediction_batch.py, which is
     the only thing that creates these; they live here so the ctypes layer can be tested on its own.
+    step is deliberately not one of the kernel's inputs - run_prediction_kernel_batch overrides it
+    with PREDICT_STEP, so it is only read when a job falls back to the Python engine.
     """
 
     __slots__ = ("charge_limit", "charge_window", "export_window", "export_limits", "pv_scenario", "end_record", "step", "soc_range_start_step", "soc_range_end_step", "sim_hash", "want_range", "range_window", "result")
@@ -280,8 +283,9 @@ def kernel_status_summary(pred):
 def load_kernel(log=None):
     """Load and verify the kernel shared library, returns the ctypes library or None.
 
-    The result is cached module-wide (including across forked workers); failures
-    are logged once and result in a permanent fallback to the Python engine.
+    The result is cached module-wide, so the library is loaded and verified once
+    per process; failures are logged once and result in a permanent fallback to
+    the Python engine.
     """
     global KERNEL_LIB, KERNEL_LOAD_TRIED, KERNEL_STATUS, KERNEL_HAS_BATCH
 
@@ -359,8 +363,11 @@ def double_array(values):
     positional arguments and is several times slower, which matters because these are rebuilt on
     every simulation. from_buffer returns a view over the array.array, and ctypes keeps the backing
     object alive through the view's _objects, so the buffer cannot be collected while the kernel is
-    using it. Each pool worker is a separate process (multiprocessing with fork), so no buffer is
-    ever shared between workers.
+    using it. The view is never written to once built: it is filled on the calling thread before
+    pk_run/pk_run_batch is entered and the kernel only ever reads it, so even a buffer deliberately
+    shared between jobs (the window memo in run_prediction_kernel_batch) is only read concurrently -
+    the kernel's worker threads never see a buffer change under them, because the one thread that
+    could change it is blocked inside the call.
 
     An iterable is taken rather than a list so callers can hand over a map/generator and skip
     materialising an intermediate list - array.array consumes it in C and coerces each item to a double
@@ -641,10 +648,12 @@ def run_prediction_kernel_batch(pred, jobs, n_threads=1):
     # covers this (b_base walks up to job_array._objects), so this list is belt-and-braces: the limit
     # arrays are held explicitly here rather than relying on ctypes' container keepalive.
     buffers = []
-    # A fan-out reuses the same window lists across most of its jobs, so their start/end arrays are
-    # marshalled once per distinct list instead of once per job - that is the bulk of the batching
-    # win. Keyed on identity, with the list retained so an id() cannot be recycled mid-batch. This
-    # relies on no caller mutating a window list between enqueue and flush (see prediction_batch.py).
+    # The charge fan-outs reuse the same window lists across every job, so their start/end arrays are
+    # marshalled once per distinct list instead of once per job - which is most of what those batches
+    # save. The export fan-out gets nothing from this: _prepare_export builds a fresh export_window
+    # list per job, so every one of those misses the memo and pays its own marshalling. Keyed on
+    # identity, with the list retained so an id() cannot be recycled mid-batch. This relies on no
+    # caller mutating a window list between enqueue and flush (see prediction_batch.py).
     window_cache = {}
 
     def window_arrays(windows):
diff --git a/docs/apps-yaml.md b/docs/apps-yaml.md
index 27022a907..770842dd3 100644
--- a/docs/apps-yaml.md
+++ b/docs/apps-yaml.md
@@ -293,7 +293,7 @@ produced at any setting - so this only trades CPU for planning time.
 Valid values are:
 
 - 'auto' - Use the same number of threads as your CPU count
-- '0' - Don't use threads - disabled
+- '0' - Clamped up to a single kernel thread, i.e. each batch is simulated serially
 - 'N' - Use N threads, recommended values are between 2 and 8
 
 ```yaml

From 8905bb59c1c59400d9b1ccad6a63d9024ec4cf44 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 21:39:04 +0100
Subject: [PATCH 18/24] docs(comments): correct references to batched
 prediction architecture

Fix two comment-only inaccuracies describing the batched prediction system
that replaced the multiprocessing pool:

1. copilot-instructions.md: replace outdated bullets describing deleted
   wrapped_run_prediction_* and PRED_GLOBAL with description of current
   launch_run_prediction_* batching mechanism.

2. prediction_kernel.py: reword window memoisation comment to accurately
   describe export window sharing, which depends on the all_n parameter.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .github/copilot-instructions.md   | 3 +--
 apps/predbat/prediction_kernel.py | 8 ++++----
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index 9ef7b3fd6..fd751f981 100644
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -274,8 +274,7 @@ self.log("Error: Failed with: {}".format(e))
 Core prediction happens in `apps/predbat/prediction.py`:
 
 - `Prediction` class runs battery simulation in 5-min steps
-- Multiprocessing via `wrapped_run_prediction_*` functions
-- Global state in `PRED_GLOBAL` dict for process sharing
+- Batched predictions via `launch_run_prediction_*()` queuing jobs and `BatchHandle.get()` flushing through the C++ kernel
 - Optimisation in `apps/predbat/plan.py` via `optimise_all_windows()`
 
 ### State Management
diff --git a/apps/predbat/prediction_kernel.py b/apps/predbat/prediction_kernel.py
index 1d265d2ed..a5b496ca0 100644
--- a/apps/predbat/prediction_kernel.py
+++ b/apps/predbat/prediction_kernel.py
@@ -650,10 +650,10 @@ def run_prediction_kernel_batch(pred, jobs, n_threads=1):
     buffers = []
     # The charge fan-outs reuse the same window lists across every job, so their start/end arrays are
     # marshalled once per distinct list instead of once per job - which is most of what those batches
-    # save. The export fan-out gets nothing from this: _prepare_export builds a fresh export_window
-    # list per job, so every one of those misses the memo and pays its own marshalling. Keyed on
-    # identity, with the list retained so an id() cannot be recycled mid-batch. This relies on no
-    # caller mutating a window list between enqueue and flush (see prediction_batch.py).
+    # save. Export windows share this memo only when all_n is truthy; otherwise _prepare_export builds
+    # a fresh one per job. Keyed on identity, with the list retained so an id() cannot be recycled
+    # mid-batch. This relies on no caller mutating a window list between enqueue and flush (see
+    # prediction_batch.py).
     window_cache = {}
 
     def window_arrays(windows):

From 1b5305ba7b1a27a746d293a52772f27d55b34fad Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 21:39:58 +0100
Subject: [PATCH 19/24] docs: record what the batching refactor measured

Keeps the numbers with the plan now the hand-off notes are gone: the serial win is real (31.6s ->
26.3s), threading is flat (~0.8%, inside noise), and the census that explains why - 25,091 batch calls
with a median of 6 jobs each, so a fresh std::thread pool is built per call to cover ~180us of work.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .../2026-08-15-phase-b-batch-prediction.md    | 43 +++++++++++++++++++
 1 file changed, 43 insertions(+)

diff --git a/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md b/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md
index 33406d018..821962474 100644
--- a/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md
+++ b/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md
@@ -1581,6 +1581,49 @@ Required: 20 scenarios unchanged with `+0.0000` on metric, cost and all three PV
 
 Then report, best of 3 each: serial batch vs threaded batch vs the 31.6s pre-Phase-B baseline.
 
+## What it actually measured, once built
+
+The plan was executed and these are the results, kept here because the hand-off notes this plan was
+written from are deleted by the last task.
+
+**The serial win is the payoff.** 20-scenario benchmark, best of 3: **26.3s** against the 31.6s
+recorded before this work (that baseline was inherited, not re-measured on the same machine). It
+comes from paying the ctypes boundary and the window marshalling once per fan-out.
+
+**Threading buys nothing measurable: 26.27s serial vs 26.05s at 16 threads**, inside the 2-6%
+run-to-run noise band. Results are bit-identical either way.
+
+**Why**, from a `n_jobs` census over one benchmark run:
+
+```text
+25,091 pk_run_batch calls carrying 253,899 jobs
+mean 10.12 jobs/call, median 6, p95 24, max 363
+97.4% of calls carry fewer than 32 jobs
+```
+
+This plan's own profile predicted fan-outs of ~1176, ~900 and ~628 jobs. The gap is mostly not a
+batching failure: the large fan-out is scoped to one `(loop_price, coarse)` iteration and its ~1176
+launches become ~363 kernel jobs once the ~44% prediction-cache hit rate and the intra-batch dedup
+have taken their share. The median of 6 is `optimise_charge_limit`'s min/max pre-pass, which is
+genuinely that small. So the batch is collecting whole fan-outs — the fan-outs are just small.
+
+At ~6 jobs of ~30us each, `pk_run_batch` builds and joins six `std::thread`s to cover ~180us of work.
+Thread setup dominates by construction.
+
+**Where the next change goes**, in order:
+
+1. **`pk_run_batch` in `prediction_kernel.cpp`** — replace the per-call `std::vector<std::thread>`
+   with a pool created once at `pk_context_create` and woken per batch. This is the smallest change
+   with the largest effect, and it makes threading pay at today's batch sizes without touching Python.
+2. **`plan.py`, not `prediction_batch.py`**, if larger batches are wanted. The ceiling is
+   `optimise_charge_limit`'s internal serialisation: the min/max pre-pass computes the SoC-pruning
+   envelope that decides which `try_socs` are launched at all. Breaking that read-then-decide
+   dependency means either launching the unpruned SoC set speculatively or running the pre-pass for
+   every window before any trial — both change optimiser semantics and risk moving plans.
+   `enqueue_prediction`/`flush_batch` already accept arbitrarily large batches; they need no change.
+3. **Not** a batch-size threshold before threading in `run_prediction_kernel_batch` — the C++ already
+   clamps threads to job count, and a Python-side knob would only hide (1).
+
 ## Notes for whoever picks this up
 
 - **`PR #4536` (`perf/window-bounds-cache`) is in flight and independent**, worth -21% on its own. Batching partly subsumes it — a batch ships window bounds once per fan-out rather than per call — but the `sim_hash` half still matters, and `prediction_cache_key()` in `prediction_batch.py` is exactly where that half would land. If both branches merge, re-profile before assuming what is left.

From 10c7f73d922a9500b2fe3877b22a4d85420f1549 Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sat, 15 Aug 2026 22:11:05 +0100
Subject: [PATCH 20/24] docs: replace the inherited baseline with a measured
 one

The 31.6s in the hand-off notes did not survive re-measurement. Against the fork point, kernel built
from each commit's own source and confirmed active on both sides: 41.74s -> 26.22s serial, 40.02s ->
25.81s at each side's default. Neither side's parallelism is worth much; the win is the batching.

Also records that the old process pool died with KeyError outside the __main__ entry point on any
spawn-default platform, which removing it fixes.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .../2026-08-15-phase-b-batch-prediction.md    | 28 +++++++++++++++----
 1 file changed, 22 insertions(+), 6 deletions(-)

diff --git a/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md b/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md
index 821962474..216198f37 100644
--- a/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md
+++ b/docs/superpowers/plans/2026-08-15-phase-b-batch-prediction.md
@@ -1586,12 +1586,28 @@ Then report, best of 3 each: serial batch vs threaded batch vs the 31.6s pre-Pha
 The plan was executed and these are the results, kept here because the hand-off notes this plan was
 written from are deleted by the last task.
 
-**The serial win is the payoff.** 20-scenario benchmark, best of 3: **26.3s** against the 31.6s
-recorded before this work (that baseline was inherited, not re-measured on the same machine). It
-comes from paying the ctypes boundary and the window marshalling once per fan-out.
-
-**Threading buys nothing measurable: 26.27s serial vs 26.05s at 16 threads**, inside the 2-6%
-run-to-run noise band. Results are bit-identical either way.
+**The win is the batching itself, and it is large.** Measured against `a131f12c`, the fork point,
+with the kernel built from each commit's own source and confirmed active on both sides, runs
+interleaved, best of 3, on a 16-CPU machine. All 20 scenarios byte-identical in every configuration.
+
+| 20-scenario benchmark | fork point | this branch | delta |
+|---|---|---|---|
+| parallelism off (`threads: 0`) | 41.74s | 26.22s | **37.2% faster** |
+| each side's default (`threads: auto`) | 40.02s | 25.81s | **35.5% faster** |
+
+The 31.6s figure in the hand-off notes was measured in an earlier session under unknown conditions
+and did not survive re-measurement — do not carry it forward.
+
+**Neither side's parallelism is worth much.** The fork point's 16-process pool bought 41.74s → 40.02s
+(4%); this branch's 16 kernel threads buy 26.22s → 25.81s (1.6%), inside the 2-6% noise band. The
+entire improvement is the batching and the marshalling it removes, not concurrency.
+
+**The fork point's process pool was also broken outside the `__main__` entry point.** Its workers
+rebuild their `Prediction` from a module global populated only in the parent, which requires `fork`
+start semantics; `hass.py` sets those only under `if __name__ == "__main__"`. On macOS (spawn by
+default since 3.8) every worker therefore started empty and every scenario died with
+`KeyError: 'dict'` — the benchmark above needed `fork` forced to get comparable numbers out of the
+baseline at all. This branch removes that failure mode by removing the pool.
 
 **Why**, from a `n_jobs` census over one benchmark run:
 

From b633d4f1e5cb6b2b4ddab54fdb97ab1eb6b2d700 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Sat, 15 Aug 2026 21:23:31 +0000
Subject: [PATCH 21/24] chore: update prediction kernel binaries for all
 platforms

---
 apps/predbat/prediction_kernel_lib_aarch64.so | Bin 247216 -> 247200 bytes
 apps/predbat/prediction_kernel_lib_armv7l.so  | Bin 197728 -> 197712 bytes
 .../prediction_kernel_lib_darwin_arm64.so     | Bin 304200 -> 304200 bytes
 .../prediction_kernel_lib_darwin_x86_64.so    | Bin 268934 -> 268934 bytes
 apps/predbat/prediction_kernel_lib_i686.so    | Bin 228908 -> 228888 bytes
 apps/predbat/prediction_kernel_lib_x86_64.so  | Bin 257112 -> 257096 bytes
 6 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/apps/predbat/prediction_kernel_lib_aarch64.so b/apps/predbat/prediction_kernel_lib_aarch64.so
index 90e1fd8ed70e4aa093223ffebb0e00edd529de70..a9e14491c592cb2a9b68cf2a202fc4143d76cb1f 100755
GIT binary patch
delta 89
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index e422a1c7c753323c33afbb80a7ff2660854629ee..d078737e80600c5668e27f7987b8a3c26852708f 100755
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_x86_64.so b/apps/predbat/prediction_kernel_lib_darwin_x86_64.so
index 7ffdaf0309992a88b4fd6757d7c4568042e70630..f09aae1a5d4fd733da68c001968220ce3c4eba2c 100755
GIT binary patch
delta 113
zcmZqMD$ur7V1fvv#YWL^cHII71_lWrW&&ac21YQ}&&bbB)h{Z|%S$cNFV8Q^)=x?-
zDJV)!nLLl(e)0o$3ju+mjRqzcZMzoBiJXnxXSHLp28RXEgytxY_9zZUAZ7w$=Iv1&
HEIL8}o~9oL

delta 115
zcmZqMD$ur7V1fvv!$#3?cB2Xg1_lWrW&&ac21YQ}4=qkDD%LM4N=?fzD$Xx0$tX|F
z$<Z$;N=->hESWr$-G1^_c54Br9ziYj8sp~Hx_7C!S8bA?tiWN>oWRkZz`+Q_OhC-M
KJ%NKoO$Y!b8X=|t

diff --git a/apps/predbat/prediction_kernel_lib_i686.so b/apps/predbat/prediction_kernel_lib_i686.so
index b91da501480cf51e8842165e239d022ae04e7cfd..b4024ef8a9524270dbaee49557cb0db63c71a849 100755
GIT binary patch
delta 93
zcmZ4Uhj+#w-U$kf4;mF)6&YI<nYJo2Pp_DMu!31w#DKvkGcP-}$V$P-$3?-&P|r}$
kVEe}kW=+QJGF8l1m>D&uf2?IzmUvLX%<!K90T`i70Hu!^0RR91

delta 107
zcmbR7hj+~%-U$kfUm6u#6&YI<nYJo2Pp`1_$j?npDoQO^NX|*jOIIjMEh^5;&r>im
z)HBqxVDQPz%T6t_Qh=-S@o|C5ZNFc^tjV~Yt%~^?Go#h?yS2>9jGv}6)-j6%0Q#mQ
A*#H0l

diff --git a/apps/predbat/prediction_kernel_lib_x86_64.so b/apps/predbat/prediction_kernel_lib_x86_64.so
index 609e1471e3da306f77b0cbe0504d525dc6c5bb1e..2dfecb538916cb4e0920d67e7d70055d53939558 100755
GIT binary patch
delta 85
zcmV-b0IL7k*bm6q50EGUh=D1EDFKBk0<|dvFXWeD<O35FFaS(xZfj+7Iv`9;L?ALT
rE-@}Jw~^!nM*)`(-~$G?+~ort1_3CS0pJ4{mk{g&AOV1vG3)~;@ZuaW

delta 116
zcmX?cga5`2{s|h4Ga5BpH5pqqnYL;&>p!vd$j?npDoQO^NX|*jOIIjMEh^5;&r>im
z)HBqxVDQPz%T6t_Qh=-S@o|C5ZLfdA?9Mn{-~lts_A^hJW!M<4rZYZZ7N7QpS%GoV
Jbj3H!>HtpMCs6<Z


From d43072e2c2e4fe8f9a5b3b60fab553325eb4949c Mon Sep 17 00:00:00 2001
From: Trefor Southwell <tdlj@tdlj.net>
Date: Sun, 16 Aug 2026 10:01:09 +0100
Subject: [PATCH 22/24] fix(kernel): bump the ABI to 4 so a pre-batch binary is
 rejected, not loaded

a3254924 made PkScenario::soc_out optional - Python passes no SoC buffer for a cached run, which is
the common case - but left PK_ABI_VERSION at 3. An ABI 3 binary writes to soc_out unconditionally, so
loading one against this Python is a null write on the first prediction: predbat segfaults instead of
falling back to the Python engine.

Reproduced by building the kernel from main and pointing PREDBAT_KERNEL_SO at it: model_kernel exits
139. With the bump the loader reports 'stale binary (abi 3 revision 5, expected abi 4 revision 5)' and
uses the Python engine, which is what the version check exists to do.

Anyone with a locally built .so from an older checkout, or an install whose per-architecture binary
was not replaced, would have hit this. All six shipped binaries rebuilt at ABI 4.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 apps/predbat/prediction_kernel.cpp            |   7 ++++++-
 apps/predbat/prediction_kernel.py             |   2 +-
 apps/predbat/prediction_kernel_lib_aarch64.so | Bin 247200 -> 247216 bytes
 apps/predbat/prediction_kernel_lib_armv7l.so  | Bin 197712 -> 197728 bytes
 .../prediction_kernel_lib_darwin_arm64.so     | Bin 304200 -> 304200 bytes
 .../prediction_kernel_lib_darwin_x86_64.so    | Bin 268934 -> 268934 bytes
 apps/predbat/prediction_kernel_lib_i686.so    | Bin 228888 -> 228908 bytes
 apps/predbat/prediction_kernel_lib_x86_64.so  | Bin 257096 -> 257112 bytes
 8 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/apps/predbat/prediction_kernel.cpp b/apps/predbat/prediction_kernel.cpp
index 79bc6c776..6b1d2bde6 100644
--- a/apps/predbat/prediction_kernel.cpp
+++ b/apps/predbat/prediction_kernel.cpp
@@ -28,7 +28,12 @@
 #include <thread>
 #include <vector>
 
-#define PK_ABI_VERSION 3
+// ABI 4: PkScenario::soc_out may be null, and pk_run_batch was added. The null is the reason this had
+// to move - Python now passes no SoC buffer for every cached run, and an ABI 3 binary writes to it
+// unconditionally, so loading one against this Python segfaults on the first prediction rather than
+// falling back. Bumping makes the loader reject it and use the Python engine, which is the whole
+// point of the check.
+#define PK_ABI_VERSION 4
 #define PK_PARITY_REVISION 5
 #define PK_MAX_CARS 4
 #define PK_RUN_EVERY 5 // const.py RUN_EVERY
diff --git a/apps/predbat/prediction_kernel.py b/apps/predbat/prediction_kernel.py
index a5b496ca0..27f5f21b9 100644
--- a/apps/predbat/prediction_kernel.py
+++ b/apps/predbat/prediction_kernel.py
@@ -31,7 +31,7 @@
 from utils import get_curve_value, find_battery_temperature_cap, in_car_slot, in_iboost_slot
 
 # Expected ABI/parity revisions of the shared library (see prediction_kernel.cpp)
-KERNEL_ABI_VERSION = 3
+KERNEL_ABI_VERSION = 4
 KERNEL_PARITY_REVISION = 5
 
 # Maximum number of cars supported by the kernel (PK_MAX_CARS in prediction_kernel.cpp)
diff --git a/apps/predbat/prediction_kernel_lib_aarch64.so b/apps/predbat/prediction_kernel_lib_aarch64.so
index a9e14491c592cb2a9b68cf2a202fc4143d76cb1f..4d99d07550a421674b22aecedbc12a4eea173605 100755
GIT binary patch
delta 117
zcmZ3`&A*|We}V?%hepk<nv9177#rH(1~39K)AqLk%!hL<J@Ru?lZsNy6_Rrj^U@W{
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JD~~d(0{|U(DUJXD

delta 99
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diff --git a/apps/predbat/prediction_kernel_lib_armv7l.so b/apps/predbat/prediction_kernel_lib_armv7l.so
index 11f649bb6018643bd06bd844ef586b6130253192..b2d0a0bd4cc8abd5a4a08d59284059f02fa523f2 100755
GIT binary patch
delta 139
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_arm64.so b/apps/predbat/prediction_kernel_lib_darwin_arm64.so
index d078737e80600c5668e27f7987b8a3c26852708f..30318c1ae3c39da63f3d2626a6a0e362c76a5ea6 100755
GIT binary patch
delta 23903
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_x86_64.so b/apps/predbat/prediction_kernel_lib_darwin_x86_64.so
index f09aae1a5d4fd733da68c001968220ce3c4eba2c..8c1909a6a64421161b9ce8001cb1a095a34a301e 100755
GIT binary patch
delta 121
zcmZqMD$ur7V1fvv!$#3?cB2Xg1_lWrW&&ac21YQ}4=qkDD%LM4N=?fzD$Xx0$tX|F
z$<Z$;N=->hESWr$-G1^_c54A4+cj%_{#oy9*yZZE$>g%sWCae3$q5{yj4aJ%9PMQs
Qj6lo;#LU~vI9LRP01YW4ivR!s

delta 119
zcmZqMD$ur7V1fvv#YWL^cHII71_lWrW&&ac21YQ}&&bbB)h{Z|%S$cNFV8Q^)=x?-
zDJV)!nLLl(e)0o$3ju+mjRqzcZMzoBiJXnxXSHLp28RXEgvn7H(u~Z_WgP8g9E?EB
N1jNkS%Q#pBgaBRXAc_D0

diff --git a/apps/predbat/prediction_kernel_lib_i686.so b/apps/predbat/prediction_kernel_lib_i686.so
index b4024ef8a9524270dbaee49557cb0db63c71a849..e25c3795dcfd32607d35e8506161ac77f27f6e3a 100755
GIT binary patch
delta 113
zcmbR7hj+~%-U$kfUp6W}e$U9#%=)37^#kK})(=dZJ1jl&b5oOwQp**Ra}x8?70ObJ
ziZk=`6pRe@4D~D+d@}R0Q;Vz=;HrFlT%dB>?^iHuGHz$9V!p=AXf^$AEweJ?r|FD!
G%;Esu%_xTe

delta 99
zcmZ4Uhj+#w-U$kf4>l@3e$U9<%=)37^#kK})(=dZJEkA3U=|iJVDQPz%T6t_Qt<I{
rQ7|&pGt@KK{;`5tlX1ID74sElMvdtoYnhcL9#k+h{AWM_Mko^iMrIwZ

diff --git a/apps/predbat/prediction_kernel_lib_x86_64.so b/apps/predbat/prediction_kernel_lib_x86_64.so
index 2dfecb538916cb4e0920d67e7d70055d53939558..8effa955d93abf831edd2132dc28fe2b34d40aff 100755
GIT binary patch
delta 126
zcmX?cga5`2{s|h4Ga5CwYBKH#Wn^i87Rm_3OxvG@G8gq(dgSM(CKaWYD<tP6=A|o?
zr4|)u=I1FG8R{A8Supry=4GcASt-C(`S`d%<+j&9VRmPnF7SYvW&4?@%rb0@R?`_D
SFpE!n!>qtKX}aPYW_19j=qzsl

delta 95
zcmca{ga5=0{s|h49gUh>H5vDWGBUS63uOdirtQx{nTz_SCq7{o7BOJ($;``6EwWPZ
z@o`ZwGSoBFGuS@y39~!nbiN17Y}@ZVWtL%M)R@lrfLVOHz#C=-#)j#JZ<y5qsb3*Y


From 9546ffec37328f0fbc64de5d8e9df2f93380d6a9 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Sun, 16 Aug 2026 09:04:32 +0000
Subject: [PATCH 23/24] chore: update prediction kernel binaries for all
 platforms

---
 apps/predbat/prediction_kernel_lib_aarch64.so | Bin 247216 -> 247200 bytes
 apps/predbat/prediction_kernel_lib_armv7l.so  | Bin 197728 -> 197712 bytes
 .../prediction_kernel_lib_darwin_arm64.so     | Bin 304200 -> 304200 bytes
 .../prediction_kernel_lib_darwin_x86_64.so    | Bin 268934 -> 268934 bytes
 apps/predbat/prediction_kernel_lib_i686.so    | Bin 228908 -> 228888 bytes
 apps/predbat/prediction_kernel_lib_x86_64.so  | Bin 257112 -> 257096 bytes
 6 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/apps/predbat/prediction_kernel_lib_aarch64.so b/apps/predbat/prediction_kernel_lib_aarch64.so
index 4d99d07550a421674b22aecedbc12a4eea173605..d226c555f1f9b3b59bb4678850a5cf3fc5ee7c8f 100755
GIT binary patch
delta 89
zcmdnc&A*_Ve}V?%gGS9(O~zJDrmdRH?R(Xea}x8?70ObJiZk=`6pRe@4D}2cd@}R0
pQ;Vz=e0*G>Lfc>NWp-!WuD73AiH%WXdf-uJbH*FfJC8D}0{}g69Vh?*

delta 107
zcmZ3`&A*|We}V?%hepj-O~zJDrmdRH?Rzaf@^e#@ic-rJl5-OC(iO^5i;6Sz^AwB>
z^$hha7<@AGvQvw!6yU0Sd|aS%+pq6sc4yqKu%B6hjnQhl<56aF##hrTk20$R0LIQE
A?f?J)

diff --git a/apps/predbat/prediction_kernel_lib_armv7l.so b/apps/predbat/prediction_kernel_lib_armv7l.so
index b2d0a0bd4cc8abd5a4a08d59284059f02fa523f2..97a7bb0dc1280cd2b7b583835b659b7f1407d977 100755
GIT binary patch
delta 112
zcmaFR!E>R5XMzIrhyP5C%B{+bTa}r7|EVYEB<7_nl%*CGXXfWA7#Zpr>KQQjWaee3
y7Fj9y__#oYwy*rhRL{6ojrkfgqsH{V%*@J+Go~xDFsn=4_{YQ`%E$l*FeU&)+#X*5

delta 130
zcmcc6!SkSlXMzGV0|RrTa;q}qR%IsNf0iEkxv5D-spSgEIf;4c3T3H9#hLke3Py%{
zhI$qZKACyhsYO-_a8*7&E>OAc)BiEmGj5e&zQW9CHT^9!vohnV>4GfG>JqR1F)@fT
JGQa_h2>^`IBRK#7

diff --git a/apps/predbat/prediction_kernel_lib_darwin_arm64.so b/apps/predbat/prediction_kernel_lib_darwin_arm64.so
index 30318c1ae3c39da63f3d2626a6a0e362c76a5ea6..43e75487739625723c7188d6e0bc05dfb7ecedc4 100755
GIT binary patch
delta 23508
zcmaJ}4OmrG*53DAN=QsFODs|-D=17fODIY-DlAeIOeoZgVM3vznPF-djS9*Nf0v{t
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_x86_64.so b/apps/predbat/prediction_kernel_lib_darwin_x86_64.so
index 8c1909a6a64421161b9ce8001cb1a095a34a301e..7563cbc48a03e27af06ac6230d107560d0d00f28 100755
GIT binary patch
delta 113
zcmZqMD$ur7V1fvv#YWL^cHII71_lWrW&&ac21YQ}&&bbB)h{Z|%S$cNFV8Q^)=x?-
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HSagH{=#U`{

delta 115
zcmZqMD$ur7V1fvv!$#3?cB2Xg1_lWrW&&ac21YQ}4=qkDD%LM4N=?fzD$Xx0$tX|F
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diff --git a/apps/predbat/prediction_kernel_lib_i686.so b/apps/predbat/prediction_kernel_lib_i686.so
index e25c3795dcfd32607d35e8506161ac77f27f6e3a..9ced39b3a6d2d9919814d50825dd1ad25c8681b3 100755
GIT binary patch
delta 93
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kVEe}kW=+QJGF8l1m>D&uf2?IzmUvLX%<!K90T`i70Hu!^0RR91

delta 107
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A*#H0l

diff --git a/apps/predbat/prediction_kernel_lib_x86_64.so b/apps/predbat/prediction_kernel_lib_x86_64.so
index 8effa955d93abf831edd2132dc28fe2b34d40aff..9ef83b0c59d7a11dd19b3f8d15bd841a7a42f912 100755
GIT binary patch
delta 85
zcmV-b0IL7k*bm6q50EGUh=D1EDFKBk0<|dvFXWeD<O35FFaS(xZfj+7Iv`9;L?ALT
rE-@}Jw~^!nM*)`(-~$G?+~ort1_3CS0pJ4{mk{g&AOV1vG3)~;@ZuaW

delta 116
zcmX?cga5`2{s|h4Ga5BpH5pqqnYL;&>p!vd$j?npDoQO^NX|*jOIIjMEh^5;&r>im
z)HBqxVDQPz%T6t_Qh=-S@o|C5ZLfdA?9Mn{-~lts_A^hJW!M<4rZYZZ7N7QpS%GoV
Jbj3H!>HtpMCs6<Z


From be9c49592ea64e22e6f6213476bb14302401173f Mon Sep 17 00:00:00 2001
From: Trefor Southwell <48591903+springfall2008@users.noreply.github.com>
Date: Sun, 16 Aug 2026 16:31:52 +0100
Subject: [PATCH 24/24] perf(kernel): persistent worker pool instead of threads
 per batch (6% faster) (#4546)

* docs: design for a persistent scratch and thread pool in the kernel

Batching made planning 35% faster but threading on top of it bought 1.6%. A census showed why: 25,091
batch calls with a median of 6 jobs, against which a fresh thread pool and a fresh 70KB scratch are
built per call. Staged so the low-risk half (per-thread scratch, which also helps the serial default)
is measured before the concurrency half.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

* perf(kernel): give each thread one scratch instead of one per call

PARKED - measured as a no-op, do not merge on its own.

Replaces the three per-call PkScratch constructions with a thread_local accessor, so a thread grows
its ~70KB of buffers once rather than once per pk_run_batch call (~25,000 of them per plan).

Measured on the serial benchmark, interleaved, best of 3: 26.291s before, 26.344s after - no
difference against ~1.8% run-to-run spread. The hypothesis was wrong: the kernel's comment about the
allocator being the bottleneck was recorded under four threads contending, and one thread repeatedly
allocating and freeing the same-sized block never leaves the allocator's thread cache.

Kept on this branch because the persistent-worker design needs per-worker scratch anyway, and the two
tests are worth having either way: one pins that the buffers stop being rebuilt per call (it fails
before this change), the other that a reused scratch cannot leak between contexts of different
n_steps (verified by mutation - relaxing the assign to skip shrinking makes it fail).

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

* perf(kernel): keep a persistent worker pool instead of threads per batch

pk_run_batch built and joined a fresh std::vector<std::thread> on every call. Measured over one
benchmark plan that was 201,545 thread creations costing 1.5s against 3.1s of simulation, which is
why threading was worth only 1.6% - the creation was cancelling what the parallelism won.

Workers now park on a condition variable for the life of the process, and each has its own flag and
condvar so a batch wakes exactly the lanes it needs. Broadcasting instead cost most of the win: a
six-job batch woke all fifteen workers, ~250,000 wasted wakeups per plan, and measured 1.3% against
3.7% for targeted wakes. The calling thread takes lane 0 itself, so a six-job batch wakes five.

Also gives each thread one scratch instead of one per call. That is a no-op on its own (26.291s vs
26.344s, inside noise - the allocator never left its thread cache) but the parked workers need it.

20-scenario benchmark, best of 3: 26.33s serial, 24.71s at 6 threads - 6.1%, on top of the batching
work. All 20 scenarios byte-identical at every thread count, in both the normal regime and one with
each job made eight times dearer.

threads: auto stays at the core count. A cap looked right on a fast machine, where the curve peaks
below it, but in the kernel-heavy regime the curve stops turning over - capping at 4 would cost 10.7%
there against 1.3% for no cap at all. resolve_batch_threads carries the numbers.

No ABI or parity bump: results are unchanged, so an older binary still loads and runs correctly.
All six shipped binaries rebuilt.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

* test(kernel): skip the pool tests when the binary predates pk_run_batch

The batch and pool tests call run_prediction_kernel_batch, which returns None when the loaded binary
has no pk_run_batch. Four of them treated that as a failure rather than a skip, so running against an
older binary reported errors instead of degrading the way the product does.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

* chore: update prediction kernel binaries for all platforms

* fix(kernel): include <system_error> explicitly

The pool catches std::system_error when the system refuses a thread. libc++ pulls that declaration in
through <thread>, libstdc++ does not, so the file built on macOS and failed on GCC with a parse error
at the catch clause. Caught by CI, which builds natively with g++; neither the local clang build nor
the zig cross-build sees it, since both use libc++.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

* chore: update prediction kernel binaries for all platforms

---------

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 .cspell/custom-dictionary-workspace.txt       |   6 +
 apps/predbat/plan.py                          |  26 +-
 apps/predbat/prediction_kernel.cpp            | 261 +++++++++++++-
 apps/predbat/prediction_kernel_lib_aarch64.so | Bin 247200 -> 253200 bytes
 apps/predbat/prediction_kernel_lib_armv7l.so  | Bin 197712 -> 203120 bytes
 .../prediction_kernel_lib_darwin_arm64.so     | Bin 304200 -> 306984 bytes
 .../prediction_kernel_lib_darwin_x86_64.so    | Bin 268934 -> 275650 bytes
 apps/predbat/prediction_kernel_lib_i686.so    | Bin 228888 -> 234508 bytes
 apps/predbat/prediction_kernel_lib_x86_64.so  | Bin 257096 -> 262568 bytes
 apps/predbat/tests/test_kernel_parity.py      | 329 ++++++++++++++++++
 apps/predbat/tests/test_prediction_batch.py   |  34 ++
 .../2026-08-16-kernel-thread-pool-design.md   | 267 ++++++++++++++
 12 files changed, 901 insertions(+), 22 deletions(-)
 create mode 100644 docs/superpowers/specs/2026-08-16-kernel-thread-pool-design.md

diff --git a/.cspell/custom-dictionary-workspace.txt b/.cspell/custom-dictionary-workspace.txt
index 6ef384d4b..d5ff0f1ef 100644
--- a/.cspell/custom-dictionary-workspace.txt
+++ b/.cspell/custom-dictionary-workspace.txt
@@ -25,6 +25,7 @@ armhf
 armv
 ASHP
 asyncio
+atfork
 authoriser
 autodocstring
 autoflake
@@ -380,6 +381,7 @@ preseeded
 prevs
 protobuf
 psum
+pthread
 pvbat
 pvenergytotal
 pvlib
@@ -537,10 +539,14 @@ venv
 Victron
 visibilitychange
 waf
+waitpid
 Wallbox
 weblink
 welink
 Werror
+WEXITSTATUS
+WIFEXITED
+WNOHANG
 workmode
 writeonly
 wrongsha
diff --git a/apps/predbat/plan.py b/apps/predbat/plan.py
index f3efa39f1..2c8c40ab6 100644
--- a/apps/predbat/plan.py
+++ b/apps/predbat/plan.py
@@ -35,6 +35,26 @@
 PLAN_PASS_WINDOW_BUDGET = 8
 
 
+def resolve_batch_threads(threads, cpu_count_value):
+    """Map the threads setting onto how many kernel lanes one batch may use.
+
+    'auto' takes the core count and is deliberately not capped. On a fast machine the curve is very
+    flat and peaks slightly below the core count - measured on the 20-scenario benchmark, best of 3:
+    serial 26.33s, 4 threads 24.89s, 6 threads 24.71s, 8 threads 24.91s, 16 threads 25.04s - so a cap
+    looks attractive. But re-running with each job made eight times dearer, which is how a machine
+    where the kernel dominates behaves, the curve stops turning over entirely: 48.92s serial, 32.03s
+    at 4, 29.98s at 6, 29.85s at 8, 28.94s at 16.
+
+    That makes the risk asymmetric. Capping at 4 costs 0.7% on the fast machine but 10.7% on the
+    kernel-heavy one, while not capping costs 1.3% at worst. The worst case for a low cap is far
+    worse than the worst case for none, so 'auto' is left alone and anyone who wants fewer lanes sets
+    threads: explicitly.
+    """
+    if threads == "auto":
+        return max(cpu_count_value, 1)
+    return max(int(threads), 1)
+
+
 def slots_around(target_slots, slot_lengths):
     """
     Return a list of slot lengths around the target slots
@@ -1306,11 +1326,7 @@ def calculate_plan(self, recompute=True, debug_mode=False, publish=True):
         # The kernel spreads one batched fan-out across threads with the GIL released for the whole
         # call, so these are real cores - unlike a Python ThreadPool, which peaked at 1.15x on two
         # threads and then degraded below serial (perf/threadpool-prototype).
-        threads = self.get_arg("threads", "auto")
-        if threads == "auto":
-            self.prediction.batch_threads = cpu_count()
-        else:
-            self.prediction.batch_threads = max(int(threads), 1)
+        self.prediction.batch_threads = resolve_batch_threads(self.get_arg("threads", "auto"), cpu_count())
         self.log("Prediction batch using {} kernel thread(s)".format(self.prediction.batch_threads))
         kernel_message, kernel_is_warning = kernel_status_summary(self.prediction)
         self.log("{}Prediction kernel: {}".format("Warn: " if kernel_is_warning else "", kernel_message))
diff --git a/apps/predbat/prediction_kernel.cpp b/apps/predbat/prediction_kernel.cpp
index 6b1d2bde6..0be4dd4f8 100644
--- a/apps/predbat/prediction_kernel.cpp
+++ b/apps/predbat/prediction_kernel.cpp
@@ -16,6 +16,8 @@
 // Build: bash apps/predbat/build_kernel.sh (g++/clang, C++17, no dependencies)
 
 #include <algorithm>
+#include <atomic>
+#include <condition_variable>
 #include <cstdint>
 #include <limits>
 #include <cstdio>
@@ -25,9 +27,16 @@
 #include <map>
 #include <memory>
 #include <mutex>
+// std::system_error, thrown when the system refuses a thread. libc++ pulls this in through <thread>,
+// libstdc++ does not, so leaving it implicit builds on macOS and fails on GCC.
+#include <system_error>
 #include <thread>
 #include <vector>
 
+#if defined(__unix__) || defined(__APPLE__)
+#include <pthread.h>
+#endif
+
 // ABI 4: PkScenario::soc_out may be null, and pk_run_batch was added. The null is the reason this had
 // to move - Python now passes no SoC buffer for every cached run, and an ABI 3 binary writes to it
 // unconditionally, so loading one against this Python segfaults on the first prediction rather than
@@ -304,12 +313,47 @@ struct PkBatchResult {
 // each scenario would otherwise heap-allocate ~70KB, and 1200 of those per batch put every worker
 // thread in contention on the allocator. Measured 4 threads at 0.6x of serial before this, because
 // the simulation is cheap enough that malloc, not arithmetic, was the bottleneck.
+//
+// See thread_scratch() below: one of these now serves every batch a thread runs, not one per call.
+
+// Counts PkScratch constructions for the life of the library, for pk_scratch_construct_count(). The
+// suite asserts this stops growing with the number of batches rather than trusting the reuse.
+std::atomic<int64_t> g_scratch_constructions{0};
+
+// Counts worker threads started for the life of the library, for pk_worker_thread_count(). Thread
+// creation measured 7.5us each and a benchmark plan used to start 201,545 of them - 1.5s of a 26s
+// plan, which is what the pool exists to stop paying. The suite asserts this stops growing.
+std::atomic<int64_t> g_worker_threads_started{0};
+
 struct PkScratch {
     std::vector<int32_t> charge_member, export_member;
     std::vector<int32_t> clipped_start, clipped_end;
     std::vector<double> clipped_limit;
+
+    PkScratch()
+    {
+        g_scratch_constructions.fetch_add(1, std::memory_order_relaxed);
+    }
 };
 
+// One scratch per thread, reused for that thread's lifetime.
+//
+// Every field is fully rewritten before it is read on each run - build_window_membership assigns
+// exactly n_steps entries and the clip helpers clear-then-push - which is the same invariant that
+// already let one scratch serve a whole stride within a batch. Extending it across calls means a
+// thread grows these buffers once instead of once per call: a benchmark plan issues ~25,000 batch
+// calls, and at a median of 6 jobs each the ~70KB allocation was the same order as the simulation it
+// wrapped.
+//
+// Thread-local rather than per-context because nothing here survives a simulation, so it never needs
+// to belong to a particular context. A thread that later runs a context with a different n_steps
+// simply has the vectors reassigned to the new length.
+static PkScratch &thread_scratch()
+{
+    static thread_local PkScratch scratch;
+    return scratch;
+}
+
 // Deep-copied context storage so Python-side buffers can be freed after create
 struct ContextStore {
     std::vector<double> rate_import, rate_export, alert_keep;
@@ -1281,8 +1325,7 @@ int32_t pk_run(int64_t handle, const PkScenario *s, PkResult *out)
     if (!c) {
         return 1;
     }
-    PkScratch scratch;
-    return pk_run_one(c, s, out, -1, -1, nullptr, nullptr, scratch);  // c is the store
+    return pk_run_one(c, s, out, -1, -1, nullptr, nullptr, thread_scratch());  // c is the store
 }
 
 // Run n_jobs scenarios against one context in a single call.
@@ -1317,6 +1360,170 @@ static void run_batch_job(const ContextStore *c, const PkBatchJob &job, PkBatchR
     out.status = pk_run_one(c, &scenario, &out.result, job.soc_range_start_step, job.soc_range_end_step, &out.soc_range_min, &out.soc_range_max, scratch);
 }
 
+
+// A pool of worker threads that outlives every batch and every context.
+//
+// pk_run_batch used to build and join a fresh std::vector<std::thread> per call. Measured over one
+// benchmark plan that was 201,545 thread creations costing 1.5s - against 3.1s of simulation, so the
+// creation cost was cancelling most of what the parallelism won and threading measured 1.6% overall.
+// Parking workers on a condition variable replaces each creation with a wake.
+//
+// Workers park until `generation` changes, then take lane `index` of a stride partition. The calling
+// thread takes lane 0 itself, so a six-job batch wakes five workers rather than six and the caller
+// does useful work instead of blocking. Nobody holds the mutex while simulating; run_batch_job only
+// reads the shared const context and writes its own result slot, which is what made the previous
+// per-call threading safe and is unchanged here.
+class PkThreadPool {
+  public:
+    // Start workers until at least `want` exist. Grows, never shrinks. Returns how many there are.
+    int32_t reserve_workers(int32_t want)
+    {
+        std::lock_guard<std::mutex> lock(mutex);
+        while (static_cast<int32_t>(workers.size()) < want) {
+            const int32_t lane = static_cast<int32_t>(workers.size()) + 1;  // lane 0 is the caller
+            std::unique_ptr<Worker> worker(new Worker());
+            try {
+                worker->thread = std::thread(&PkThreadPool::worker_loop, this, lane, worker.get());
+            } catch (const std::system_error &) {
+                // Out of thread resources - keep what we have and let the caller use fewer lanes.
+                break;
+            }
+            workers.push_back(std::move(worker));
+            g_worker_threads_started.fetch_add(1, std::memory_order_relaxed);
+        }
+        return static_cast<int32_t>(workers.size());
+    }
+
+    // Run jobs[0..n_jobs) across `use` lanes, the calling thread taking lane 0. `use` must be at
+    // least 1 and no more than worker_count() + 1.
+    void run(const ContextStore *c, const PkBatchJob *jobs, int32_t n_jobs, PkBatchResult *results, int32_t use)
+    {
+        {
+            std::lock_guard<std::mutex> lock(mutex);
+            cur_ctx = c;
+            cur_jobs = jobs;
+            cur_results = results;
+            cur_n_jobs = n_jobs;
+            cur_use = use;
+            outstanding = use - 1;
+            // Each worker has its own flag and its own condition variable, so exactly the lanes this
+            // batch needs are woken. Broadcasting instead would wake every worker in the pool - with
+            // a median batch of six jobs against sixteen workers that was ~250,000 wakeups per plan
+            // spent entirely on threads that immediately went back to sleep.
+            for (int32_t lane = 1; lane < use; lane++) {
+                workers[lane - 1]->has_work = true;
+            }
+        }
+        for (int32_t lane = 1; lane < use; lane++) {
+            workers[lane - 1]->ready.notify_one();
+        }
+
+        PkScratch &scratch = thread_scratch();
+        for (int32_t i = 0; i < n_jobs; i += use) {
+            run_batch_job(c, jobs[i], results[i], scratch);
+        }
+
+        std::unique_lock<std::mutex> lock(mutex);
+        work_done.wait(lock, [this] { return outstanding == 0; });
+    }
+
+  private:
+    // One parked worker. The flag is written under the pool mutex before its condition variable is
+    // signalled, so a wake cannot be lost even if the worker has not re-parked yet.
+    struct Worker {
+        std::condition_variable ready;
+        bool has_work = false;
+        std::thread thread;
+    };
+
+    void worker_loop(int32_t lane, Worker *self)
+    {
+        std::unique_lock<std::mutex> lock(mutex);
+        for (;;) {
+            self->ready.wait(lock, [self] { return self->has_work; });
+            self->has_work = false;
+            const ContextStore *c = cur_ctx;
+            const PkBatchJob *jobs = cur_jobs;
+            PkBatchResult *results = cur_results;
+            const int32_t n_jobs = cur_n_jobs;
+            const int32_t use = cur_use;
+            lock.unlock();
+
+            PkScratch &scratch = thread_scratch();
+            for (int32_t i = lane; i < n_jobs; i += use) {
+                run_batch_job(c, jobs[i], results[i], scratch);
+            }
+
+            lock.lock();
+            if (--outstanding == 0) {
+                work_done.notify_one();
+            }
+        }
+    }
+
+    std::mutex mutex;
+    std::condition_variable work_done;  // the caller waits here
+    const ContextStore *cur_ctx = nullptr;
+    const PkBatchJob *cur_jobs = nullptr;
+    PkBatchResult *cur_results = nullptr;
+    int32_t cur_n_jobs = 0;
+    int32_t cur_use = 1;
+    int32_t outstanding = 0;
+    std::vector<std::unique_ptr<Worker>> workers;
+};
+
+// The pool is created on first threaded use and deliberately never destroyed: a static destructor
+// running while CPython tears the process down risks a deadlock for no benefit, and the OS reclaims
+// the threads at exit.
+std::atomic<PkThreadPool *> g_pool{nullptr};
+std::mutex g_pool_create_mutex;
+// Held for a whole batch. Python can enter pk_run_batch from two threads at once because ctypes
+// releases the GIL, and one shared pool cannot serve two batches concurrently - its published job
+// state would race. Nothing in predbat does this today; serialising is the safe answer if it ever does.
+std::mutex g_dispatch_mutex;
+
+#if defined(__unix__) || defined(__APPLE__)
+// Threads do not survive fork(), so a child would inherit a pool whose workers do not exist and hang
+// waiting for them. The prepare/parent pair keep the dispatch mutex consistent across the fork, and
+// the child drops the pool so its next batch builds a fresh one. Nothing here allocates or takes a
+// new lock, which is what the child handler is allowed to do; the dead pool leaks in the child.
+void pk_atfork_prepare()
+{
+    g_dispatch_mutex.lock();
+}
+
+void pk_atfork_parent()
+{
+    g_dispatch_mutex.unlock();
+}
+
+void pk_atfork_child()
+{
+    g_dispatch_mutex.unlock();
+    g_pool.store(nullptr, std::memory_order_relaxed);
+}
+#endif
+
+// Fetch the pool, creating it on first use.
+static PkThreadPool *pool_singleton()
+{
+    PkThreadPool *pool = g_pool.load(std::memory_order_acquire);
+    if (pool) {
+        return pool;
+    }
+    std::lock_guard<std::mutex> lock(g_pool_create_mutex);
+    pool = g_pool.load(std::memory_order_relaxed);
+    if (!pool) {
+#if defined(__unix__) || defined(__APPLE__)
+        static std::once_flag atfork_once;
+        std::call_once(atfork_once, [] { pthread_atfork(pk_atfork_prepare, pk_atfork_parent, pk_atfork_child); });
+#endif
+        pool = new PkThreadPool();
+        g_pool.store(pool, std::memory_order_release);
+    }
+    return pool;
+}
+
 // Run n_jobs scenarios against one context in a single call.
 //
 // The context lookup and the Python/C boundary are paid once for a whole fan-out rather than once
@@ -1338,30 +1545,50 @@ int32_t pk_run_batch(int64_t handle, const PkBatchJob *jobs, int32_t n_jobs, PkB
     // Scenarios are independent - each reads the shared const context and writes only its own result
     // slot - so they are split across threads by stride.
     if (n_threads > 1 && n_jobs > 1) {
-        const int32_t use = n_threads < n_jobs ? n_threads : n_jobs;
-        std::vector<std::thread> pool;
-        pool.reserve(use);
-        for (int32_t t = 0; t < use; t++) {
-            pool.emplace_back([c, jobs, results, n_jobs, use, t]() {
-                PkScratch scratch;
-                for (int32_t i = t; i < n_jobs; i += use) {
-                    run_batch_job(c, jobs[i], results[i], scratch);
-                }
-            });
+        std::lock_guard<std::mutex> dispatch(g_dispatch_mutex);
+        int32_t use = n_threads < n_jobs ? n_threads : n_jobs;
+        PkThreadPool *pool = pool_singleton();
+        // Clamp to the lanes that actually exist: reserve_workers stops early if the system refuses a
+        // thread, and a batch must never wait on a worker that was never started.
+        const int32_t lanes = pool->reserve_workers(use - 1) + 1;
+        if (use > lanes) {
+            use = lanes;
         }
-        for (auto &th : pool) {
-            th.join();
+        if (use > 1) {
+            pool->run(c, jobs, n_jobs, results, use);
+            return 0;
         }
-        return 0;
+        // No workers available - fall through and run the batch inline.
     }
-    PkScratch scratch;
     for (int32_t i = 0; i < n_jobs; i++) {
-        run_batch_job(c, jobs[i], results[i], scratch);
+        run_batch_job(c, jobs[i], results[i], thread_scratch());
     }
     return 0;
 }
 
 
+// Test hook: how many PkScratch instances have been constructed since the library was loaded.
+//
+// The scratch buffers are the only heap allocation a simulation makes, and the kernel's whole
+// batching win rests on not paying them per call - so the suite asserts this counter stops growing
+// with the number of batches rather than trusting the code to be reusing them.
+int64_t pk_scratch_construct_count(void)
+{
+    return g_scratch_constructions.load(std::memory_order_relaxed);
+}
+
+
+// Test hook: how many worker threads have been started since the library was loaded.
+//
+// Batches used to build and join a fresh set of threads on every call. The suite asserts this count
+// stops growing once the pool is warm, because a thread creation costs several microseconds against
+// a median batch of six jobs - it was cancelling the parallelism it was there to provide.
+int64_t pk_worker_thread_count(void)
+{
+    return g_worker_threads_started.load(std::memory_order_relaxed);
+}
+
+
 // Test hook: sweep SoC densely and confirm the precomputed bucket boundaries give exactly the same
 // percent as the round_py path they replace. Returns the number of disagreements (0 = equivalent).
 int32_t pk_verify_soc_percent_table(double soc_max, int32_t samples)
diff --git a/apps/predbat/prediction_kernel_lib_aarch64.so b/apps/predbat/prediction_kernel_lib_aarch64.so
index d226c555f1f9b3b59bb4678850a5cf3fc5ee7c8f..eda553983b4d496fbbdd4783e419f4527d34d321 100755
GIT binary patch
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diff --git a/apps/predbat/prediction_kernel_lib_armv7l.so b/apps/predbat/prediction_kernel_lib_armv7l.so
index 97a7bb0dc1280cd2b7b583835b659b7f1407d977..b26eda510a186c9c80e7a3a22e70eec5fd3d45ee 100755
GIT binary patch
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_arm64.so b/apps/predbat/prediction_kernel_lib_darwin_arm64.so
index 43e75487739625723c7188d6e0bc05dfb7ecedc4..0431deb3d5a40fa85d8ced7e1702afbb2db22ce9 100755
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diff --git a/apps/predbat/prediction_kernel_lib_darwin_x86_64.so b/apps/predbat/prediction_kernel_lib_darwin_x86_64.so
index 7563cbc48a03e27af06ac6230d107560d0d00f28..7300ca9bd6ac95374661e119b6a8a4c813d7b3fc 100755
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diff --git a/apps/predbat/prediction_kernel_lib_i686.so b/apps/predbat/prediction_kernel_lib_i686.so
index 9ced39b3a6d2d9919814d50825dd1ad25c8681b3..05939aabcc9d8babdef862e46b0561031436f42b 100755
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diff --git a/apps/predbat/prediction_kernel_lib_x86_64.so b/apps/predbat/prediction_kernel_lib_x86_64.so
index 9ef83b0c59d7a11dd19b3f8d15bd841a7a42f912..9cb9287e0b45d5d5689b5354b68a43ab6eabdf96 100755
GIT binary patch
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diff --git a/apps/predbat/tests/test_kernel_parity.py b/apps/predbat/tests/test_kernel_parity.py
index 3183e6cdf..bdcd49025 100644
--- a/apps/predbat/tests/test_kernel_parity.py
+++ b/apps/predbat/tests/test_kernel_parity.py
@@ -26,7 +26,11 @@
 import gc
 import os
 import random
+import signal
 import subprocess
+import sys
+import threading
+import time
 
 import prediction_kernel
 from const import PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10, PV_SCENARIO_PV90
@@ -1124,6 +1128,319 @@ def run_soc_percent_table_tests():
     return failed
 
 
+def run_scratch_reuse_tests(my_predbat, batches=10):
+    """Check a thread builds its scratch buffers once, not once per batch, returns True on failure.
+
+    The scratch vectors are the only heap allocation a simulation makes - roughly 70KB once they have
+    grown to n_steps - and a benchmark plan issues around 25,000 batch calls, so paying that per call
+    is the difference between the allocator and the arithmetic being the bottleneck. The kernel
+    exposes pk_scratch_construct_count for this: the count must stop growing with the number of
+    batches rather than tracking it.
+    """
+    print("**** Running kernel scratch reuse tests ****")
+    lib = prediction_kernel.KERNEL_LIB
+    if not lib or not hasattr(lib, "pk_scratch_construct_count"):
+        print("SKIP: kernel does not expose pk_scratch_construct_count")
+        return False
+
+    lib.pk_scratch_construct_count.restype = ctypes.c_int64
+    lib.pk_scratch_construct_count.argtypes = []
+
+    prediction, jobs = build_batch_jobs(my_predbat, random.Random(99), 4)
+    if not prediction.kernel_handle:
+        print("ERROR: scratch reuse kernel context creation failed")
+        return True
+
+    # One batch first, so any first-touch allocation is already paid before the count is taken
+    prediction_kernel.run_prediction_kernel_batch(prediction, jobs, 1)
+    before = lib.pk_scratch_construct_count()
+    for _ in range(batches):
+        if prediction_kernel.run_prediction_kernel_batch(prediction, jobs, 1) is None:
+            print("ERROR: scratch reuse batch call failed")
+            return True
+    grew = lib.pk_scratch_construct_count() - before
+
+    if grew:
+        print("ERROR: {} batches built {} scratch buffers - they should be reused, not rebuilt per call".format(batches, grew))
+        return True
+    print("Scratch reuse: {} batches on one thread built 0 further scratch buffers".format(batches))
+    return False
+
+
+def run_scratch_cross_context_tests(my_predbat, rounds=3):
+    """Check a reused scratch cannot leak between contexts of different sizes, returns True on failure.
+
+    One scratch now serves every batch a thread ever runs, and Predbat builds a fresh context each
+    plan cycle - with a different n_steps whenever forecast_minutes changes. The buffers are only safe
+    to reuse because every field is fully rewritten before it is read: build_window_membership assigns
+    exactly n_steps entries and the clip helpers clear before pushing. If that ever stopped holding, a
+    large context followed by a small one would read the large one's tail.
+
+    So the contexts are deliberately run largest-first and then interleaved, and each must keep
+    returning what it returned on its own.
+    """
+    print("**** Running kernel scratch cross-context tests ****")
+    if not prediction_kernel.KERNEL_HAS_BATCH:
+        print("SKIP: kernel does not expose pk_run_batch")
+        return False
+
+    saved_forecast = my_predbat.forecast_minutes
+    try:
+        cases = []
+        # Largest first: a later, smaller context is the one that would read a stale tail
+        for forecast_minutes in (48 * 60, 12 * 60):
+            my_predbat.forecast_minutes = forecast_minutes
+            prediction, jobs = build_batch_jobs(my_predbat, random.Random(forecast_minutes), 6)
+            if not prediction.kernel_handle:
+                print("ERROR: cross-context kernel context creation failed for {} minutes".format(forecast_minutes))
+                return True
+            cases.append((forecast_minutes, prediction, jobs, build_batch_reference(prediction, jobs)))
+
+        failed = False
+        for round_n in range(rounds):
+            for forecast_minutes, prediction, jobs, reference in cases:
+                batched = prediction_kernel.run_prediction_kernel_batch(prediction, jobs, 1)
+                if batched is None:
+                    print("ERROR: cross-context batch call failed for {} minutes".format(forecast_minutes))
+                    return True
+                for index, (result_tuple, soc_range_min, soc_range_max) in enumerate(batched):
+                    single, (expect_min, expect_max) = reference[index]
+                    if result_tuple is None:
+                        print("ERROR: round {} {} minutes job {} reported a non-zero status".format(round_n, forecast_minutes, index))
+                        failed = True
+                        continue
+                    for field, name in enumerate(RESULT_NAMES):
+                        if result_tuple[field] != single[field]:
+                            print("ERROR: round {} {} minutes job {} differs on {}: got {} want {}".format(round_n, forecast_minutes, index, name, result_tuple[field], single[field]))
+                            failed = True
+                    if soc_range_min != expect_min or soc_range_max != expect_max:
+                        print("ERROR: round {} {} minutes job {} SoC range {} {} want {} {}".format(round_n, forecast_minutes, index, soc_range_min, soc_range_max, expect_min, expect_max))
+                        failed = True
+            if failed:
+                break
+
+        if not failed:
+            print("Scratch cross-context: {} rounds interleaving {} and {} minute contexts, all identical".format(rounds, cases[0][0], cases[1][0]))
+        return failed
+    finally:
+        my_predbat.forecast_minutes = saved_forecast
+
+
+def run_worker_reuse_tests(my_predbat, batches=10, n_threads=4):
+    """Check threaded batches reuse parked workers instead of starting new ones, returns True on failure.
+
+    A benchmark plan issues ~25,000 threaded batch calls with a median of six jobs each. Starting a
+    thread per lane per call measured 201,545 threads and 1.5s of a 26s plan - more than the
+    parallelism was returning, which is why threading was worth 1.6% before the pool. The kernel
+    exposes pk_worker_thread_count for this: once warm, the count must stop growing with the number of
+    batches.
+    """
+    print("**** Running kernel worker reuse tests ****")
+    lib = prediction_kernel.KERNEL_LIB
+    if not lib or not hasattr(lib, "pk_worker_thread_count"):
+        print("SKIP: kernel does not expose pk_worker_thread_count")
+        return False
+
+    lib.pk_worker_thread_count.restype = ctypes.c_int64
+    lib.pk_worker_thread_count.argtypes = []
+
+    prediction, jobs = build_batch_jobs(my_predbat, random.Random(1234), 8)
+    if not prediction.kernel_handle:
+        print("ERROR: worker reuse kernel context creation failed")
+        return True
+
+    # Warm first, so the pool has grown to n_threads before the count is taken
+    prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads)
+    before = lib.pk_worker_thread_count()
+    for _ in range(batches):
+        if prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads) is None:
+            print("ERROR: worker reuse batch call failed")
+            return True
+    grew = lib.pk_worker_thread_count() - before
+
+    if grew:
+        print("ERROR: {} batches at {} threads started {} more workers - they should be reused, not restarted per call".format(batches, n_threads, grew))
+        return True
+    print("Worker reuse: {} batches at {} threads started 0 further workers".format(batches, n_threads))
+    return False
+
+
+def compare_batch_to_reference(label, batched, reference):
+    """Compare a batch's results against a per-job pk_run reference, returns True on failure"""
+    failed = False
+    for index, (result_tuple, soc_range_min, soc_range_max) in enumerate(batched):
+        single, (expect_min, expect_max) = reference[index]
+        if result_tuple is None:
+            print("ERROR: {} job {} reported a non-zero status".format(label, index))
+            failed = True
+            continue
+        for field, name in enumerate(RESULT_NAMES):
+            if result_tuple[field] != single[field]:
+                print("ERROR: {} job {} differs on {}: got {} want {}".format(label, index, name, result_tuple[field], single[field]))
+                failed = True
+        if soc_range_min != expect_min or soc_range_max != expect_max:
+            print("ERROR: {} job {} SoC range {} {} want {} {}".format(label, index, soc_range_min, soc_range_max, expect_min, expect_max))
+            failed = True
+    return failed
+
+
+def run_pool_lane_tests(my_predbat):
+    """Check lane allocation across batch and pool sizes, returns True on failure.
+
+    The pool is sized by the largest thread count it has been asked for and never shrinks, so most
+    batches use fewer lanes than there are workers - the median batch is six jobs against a pool built
+    for more. Idle workers must sit out without touching the completion count, and a batch must never
+    wait on a lane that was never dispatched. These combinations walk a batch smaller than the pool,
+    the pool growing, the pool being asked for less than it has, and the single-job and single-thread
+    bypasses.
+    """
+    print("**** Running kernel pool lane tests ****")
+    if not prediction_kernel.KERNEL_HAS_BATCH:
+        print("SKIP: kernel does not expose pk_run_batch")
+        return False
+
+    prediction, jobs = build_batch_jobs(my_predbat, random.Random(2468), 8)
+    if not prediction.kernel_handle:
+        print("ERROR: pool lane kernel context creation failed")
+        return True
+    reference = build_batch_reference(prediction, jobs)
+
+    failed = False
+    for n_threads, n_jobs in ((16, 3), (8, 8), (2, 8), (16, 8), (4, 1), (1, 8), (3, 8)):
+        subset = jobs[:n_jobs]
+        batched = prediction_kernel.run_prediction_kernel_batch(prediction, subset, n_threads)
+        if batched is None:
+            print("ERROR: pool lane batch failed at {} threads {} jobs".format(n_threads, n_jobs))
+            return True
+        failed |= compare_batch_to_reference("threads={} jobs={}".format(n_threads, n_jobs), batched, reference[:n_jobs])
+
+    if not failed:
+        print("Pool lanes: 7 thread/job combinations all bit-identical to pk_run")
+    return failed
+
+
+def run_pool_concurrency_tests(my_predbat, rounds=400, n_threads=8):
+    """Check two Python threads can dispatch batches at once, returns True on failure.
+
+    ctypes releases the GIL, so predbat could enter pk_run_batch from two threads simultaneously. One
+    shared pool cannot serve two batches at once - the published job pointers would race - so dispatch
+    is serialised by a mutex. Nothing does this today, which is why it needs a test rather than an
+    assumption: each thread checks its own job set against its own reference, so a crossed batch shows
+    up as wrong values, not just a crash.
+
+    The round count is load-bearing and was chosen by mutation, not taste. With the dispatch mutex
+    deleted, 25 rounds over 4 lanes passes happily - the GIL keeps the two callers from overlapping
+    often enough for the race to land - while 400 rounds over 8 lanes deadlocks within seconds and is
+    caught by the join timeout below. The healthy case costs no measurable time either way, so do not
+    trim these numbers to make the suite look faster.
+    """
+    print("**** Running kernel pool concurrency tests ****")
+    if not prediction_kernel.KERNEL_HAS_BATCH:
+        print("SKIP: kernel does not expose pk_run_batch")
+        return False
+
+    sets = []
+    for seed in (11, 22):
+        prediction, jobs = build_batch_jobs(my_predbat, random.Random(seed), 6)
+        if not prediction.kernel_handle:
+            print("ERROR: pool concurrency kernel context creation failed")
+            return True
+        sets.append((prediction, jobs, build_batch_reference(prediction, jobs)))
+
+    errors = []
+
+    def hammer(label, prediction, jobs, reference):
+        """Run many batches on one context and record any mismatch against its own reference"""
+        for _ in range(rounds):
+            batched = prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads)
+            if batched is None:
+                errors.append("{}: batch call failed".format(label))
+                return
+            if compare_batch_to_reference(label, batched, reference):
+                errors.append("{}: result mismatch".format(label))
+                return
+
+    workers = [threading.Thread(target=hammer, args=("concurrent-{}".format(n), prediction, jobs, reference)) for n, (prediction, jobs, reference) in enumerate(sets)]
+    for worker in workers:
+        worker.start()
+    for worker in workers:
+        worker.join(timeout=120)
+    for worker in workers:
+        if worker.is_alive():
+            print("ERROR: concurrent dispatch did not finish within 120s - suspect a deadlock")
+            return True
+
+    if errors:
+        for error in errors:
+            print("ERROR: {}".format(error))
+        return True
+    print("Pool concurrency: 2 threads x {} batches, all bit-identical to pk_run".format(rounds))
+    return False
+
+
+def run_pool_fork_tests(my_predbat, n_threads=4, timeout_s=60):
+    """Check a batch still runs in a forked child, returns True on failure.
+
+    Threads do not survive fork(), so a child inherits a pool whose workers do not exist. Without the
+    pthread_atfork handler that drops the pool, the child's first batch waits forever on lanes that
+    will never report - a hang, not a crash, which is why this test kills the child on a deadline and
+    treats the timeout as the failure rather than waiting on it.
+    """
+    print("**** Running kernel pool fork tests ****")
+    if not prediction_kernel.KERNEL_HAS_BATCH:
+        print("SKIP: kernel does not expose pk_run_batch")
+        return False
+
+    if not hasattr(os, "fork"):
+        print("SKIP: platform has no fork()")
+        return False
+
+    prediction, jobs = build_batch_jobs(my_predbat, random.Random(3690), 6)
+    if not prediction.kernel_handle:
+        print("ERROR: pool fork kernel context creation failed")
+        return True
+    reference = build_batch_reference(prediction, jobs)
+
+    # Warm the pool in the parent, so the child inherits one whose workers are gone
+    if prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads) is None:
+        print("ERROR: pool fork parent warm-up batch failed")
+        return True
+
+    sys.stdout.flush()
+    sys.stderr.flush()
+    pid = os.fork()
+    if pid == 0:
+        # Child: os._exit throughout, so the parent's buffers are never flushed twice
+        try:
+            batched = prediction_kernel.run_prediction_kernel_batch(prediction, jobs, n_threads)
+            if batched is None:
+                os._exit(3)
+            os._exit(1 if compare_batch_to_reference("fork-child", batched, reference) else 0)
+        except BaseException:
+            os._exit(2)
+
+    deadline = time.time() + timeout_s
+    status = None
+    while time.time() < deadline:
+        waited, raw_status = os.waitpid(pid, os.WNOHANG)
+        if waited == pid:
+            status = raw_status
+            break
+        time.sleep(0.05)
+
+    if status is None:
+        os.kill(pid, signal.SIGKILL)
+        os.waitpid(pid, 0)
+        print("ERROR: forked child did not finish within {}s - the inherited pool deadlocked".format(timeout_s))
+        return True
+    if not os.WIFEXITED(status) or os.WEXITSTATUS(status) != 0:
+        print("ERROR: forked child exited with status {} (1 = mismatch, 2 = exception, 3 = batch failed)".format(status))
+        return True
+
+    print("Pool fork: a batch in a forked child matched pk_run")
+    return False
+
+
 def run_kernel_parity_tests(my_predbat):
     """Compare the C++ prediction kernel against the Python engine, returns True on failure"""
     print("**** Running kernel parity tests ****")
@@ -1145,6 +1462,18 @@ def run_kernel_parity_tests(my_predbat):
             failed |= run_batch_parity_tests(my_predbat)
         if not failed:
             failed |= run_batch_window_variant_tests(my_predbat)
+        if not failed:
+            failed |= run_scratch_reuse_tests(my_predbat)
+        if not failed:
+            failed |= run_scratch_cross_context_tests(my_predbat)
+        if not failed:
+            failed |= run_worker_reuse_tests(my_predbat)
+        if not failed:
+            failed |= run_pool_lane_tests(my_predbat)
+        if not failed:
+            failed |= run_pool_concurrency_tests(my_predbat)
+        if not failed:
+            failed |= run_pool_fork_tests(my_predbat)
     finally:
         restore_scenario_state(my_predbat, state)
 
diff --git a/apps/predbat/tests/test_prediction_batch.py b/apps/predbat/tests/test_prediction_batch.py
index 36bcb3184..3bfd2046b 100644
--- a/apps/predbat/tests/test_prediction_batch.py
+++ b/apps/predbat/tests/test_prediction_batch.py
@@ -24,6 +24,7 @@
 
 import prediction_batch
 from const import PREDICT_STEP, PV_SCENARIO_NOMINAL, PV_SCENARIO_PV10
+from plan import resolve_batch_threads
 from prediction import Prediction
 from prediction_kernel import create_kernel_context
 from tests.test_kernel_parity import apply_random_scenario, kernel_available, make_step_data, make_windows, restore_scenario_state, snapshot_scenario_state
@@ -511,9 +512,42 @@ def test_batch_results_match_their_own_job(my_predbat):
     return failed
 
 
+def test_batch_thread_count_resolution():
+    """Check how the threads setting maps onto kernel lanes, returns True on failure.
+
+    'auto' takes the core count uncapped. A cap was measured and rejected: on a fast machine the
+    curve is flat and peaks slightly below the core count, but with each job made eight times dearer
+    - how a machine where the kernel dominates behaves - it stops turning over, and a cap of 4 then
+    costs 10.7% against 1.3% for no cap at all. The zero and one cases both mean serial, which is what
+    annual.py and the benchmark harness rely on.
+    """
+    print("**** Running batch thread count resolution tests ****")
+    failed = False
+    cases = [
+        ("auto", 2, 2, "auto follows the core count"),
+        ("auto", 16, 16, "auto is not capped"),
+        ("auto", 64, 64, "auto is not capped on a large machine either"),
+        ("auto", 0, 1, "a nonsense core count still leaves one usable lane"),
+        (0, 32, 1, "0 means serial"),
+        (1, 32, 1, "1 means serial"),
+        (2, 32, 2, "an explicit value is honoured"),
+        (12, 32, 12, "an explicit value above the core count is honoured"),
+    ]
+    for threads, cores, expected, why in cases:
+        got = resolve_batch_threads(threads, cores)
+        if got != expected:
+            print("ERROR: threads={} cores={} gave {} lanes, expected {} ({})".format(threads, cores, got, expected, why))
+            failed = True
+
+    if not failed:
+        print("Batch thread count resolution tests passed")
+    return failed
+
+
 def run_prediction_batch_tests(my_predbat):
     """Run every batched prediction test, returns True on failure"""
     failed = test_export_trial_does_not_mutate_caller_window(my_predbat)
+    failed |= test_batch_thread_count_resolution()
     failed |= test_batch_state_exists_without_a_base(my_predbat)
 
     available, required_failure = kernel_available()
diff --git a/docs/superpowers/specs/2026-08-16-kernel-thread-pool-design.md b/docs/superpowers/specs/2026-08-16-kernel-thread-pool-design.md
new file mode 100644
index 000000000..7c4d3986b
--- /dev/null
+++ b/docs/superpowers/specs/2026-08-16-kernel-thread-pool-design.md
@@ -0,0 +1,267 @@
+# Persistent scratch and thread pool in the prediction kernel
+
+**Status:** implemented. Three things below were changed by measurement during implementation and are
+recorded in place: stage 1 is a no-op on its own, broadcasting to wake workers was the wrong choice,
+and the `threads: auto` cap this design would have added was measured and rejected. See "What
+measurement changed" at the end.
+
+**Branch:** `perf/kernel-thread-pool`, stacked on `perf/kernel-batch-abi` (PR #4540). Neither
+`pk_run_batch` nor `PkScratch` exists on `main` — both arrive with that PR — so this work cannot be
+based on `main`. The follow-up PR takes `perf/kernel-batch-abi` as its base so its diff shows only
+this change, and is retargeted to `main` once #4540 merges.
+
+## Why
+
+PR #4540 replaced the per-scenario prediction fan-out with a batch that reaches the C++ kernel in one
+`pk_run_batch` call. It made planning 35-37% faster, but enabling kernel threading on top of it
+bought only 1.6% — inside the run-to-run noise band.
+
+A census over one 20-scenario benchmark run explains why:
+
+```text
+25,091 pk_run_batch calls carrying 253,899 jobs
+mean 10.12 jobs/call, median 6, p95 24, max 363
+97.4% of calls carry fewer than 32 jobs
+```
+
+The median batch is six jobs of roughly 30us each — about 180us of simulation. Against that,
+`pk_run_batch` builds and joins a fresh `std::vector<std::thread>` on every call, and the serial path
+constructs a fresh `PkScratch` whose first use heap-allocates roughly 70KB. Both costs are paid per
+call, and at these batch sizes both are the same order as the work they wrap.
+
+The kernel's own comment already records that this class of cost dominates: scratch reuse was
+introduced because "each scenario would otherwise heap-allocate ~70KB, and 1200 of those per batch
+put every worker thread in contention on the allocator. Measured 4 threads at 0.6x of serial before
+this, because the simulation is cheap enough that malloc, not arithmetic, was the bottleneck."
+
+This design removes both per-call costs. It is deliberately staged, because the two halves carry very
+different risk and the first may be enough on its own.
+
+## Scope
+
+**In scope:** `apps/predbat/prediction_kernel.cpp`, its tests, and the six shipped binaries.
+
+**Out of scope:** any Python change. `n_threads` stays a per-call argument, the struct layouts do not
+move, and the function signatures are unchanged, so `prediction_kernel.py` is untouched.
+
+**Not an ABI or parity change.** Results are bit-identical before and after, so neither
+`PK_ABI_VERSION` nor `PK_PARITY_REVISION` is bumped. A binary built before this change still loads
+and runs correctly, just without the speedup. The six architecture binaries are still rebuilt and
+shipped.
+
+## Stage 1 — one scratch per thread
+
+`PkScratch` is constructed at three sites: `pk_run` (prediction_kernel.cpp:1279), each worker lambda
+in `pk_run_batch` (:1341), and the serial batch loop (:1352). All three become one accessor:
+
+```cpp
+// One scratch per thread, reused for that thread's lifetime. Every field is fully rewritten before
+// it is read on each run - build_window_membership assigns exactly n_steps entries and the clip
+// helpers clear-then-push - which is the same invariant that already lets one scratch serve a whole
+// stride. Extending it across calls means a thread allocates these buffers once, not once per call.
+// Thread-local rather than per-context because nothing here survives a simulation, so it never needs
+// to belong to a particular context.
+static PkScratch &thread_scratch()
+{
+    static thread_local PkScratch scratch;
+    return scratch;
+}
+```
+
+Each of the three sites drops its local and passes `thread_scratch()` instead.
+
+### The risk this introduces
+
+Reuse within a call spans one context, so `n_steps` is constant. Reuse across calls spans contexts,
+and Predbat builds a new context every plan cycle — with a different `n_steps` whenever
+`forecast_minutes` changes. `build_window_membership` assigns exactly `n_steps` entries and the clip
+vectors clear before pushing, so a shrink cannot expose stale tail data through any read path.
+
+That is an argument, not a proof, so it is pinned by a test (see Testing).
+
+### Expected effect
+
+Removes roughly 25,000 allocations of ~70KB per plan from the serial path — which is the default for
+`threads: 0`, for `annual.py`, and for the whole test harness. Independently measurable and
+independently shippable.
+
+## Stage 2 — one pool for the process
+
+A single pool, allocated on first threaded use and never destroyed, holding workers parked on a
+condition variable.
+
+```cpp
+class PkThreadPool {
+public:
+    void reserve_workers(int32_t want);   // grows, never shrinks
+    void run(const ContextStore *c, const PkBatchJob *jobs, int32_t n_jobs, PkBatchResult *results, int32_t use);
+private:
+    std::mutex mutex;
+    std::condition_variable work_ready;   // workers park here
+    std::condition_variable work_done;    // the caller waits here
+    const ContextStore *cur_ctx = nullptr;
+    const PkBatchJob *cur_jobs = nullptr;
+    PkBatchResult *cur_results = nullptr;
+    int32_t cur_n_jobs = 0;
+    int32_t cur_use = 1;
+    uint64_t generation = 0;              // bumped per batch; workers wake when it changes
+    int32_t outstanding = 0;              // lanes still running
+    std::vector<std::thread> workers;
+};
+```
+
+### Dispatch
+
+The lane count is `use = min(n_threads, n_jobs, workers.size() + 1)` — bounded by what the caller
+asked for, by how much work there is, and by how many workers actually exist (see Thread creation
+failure). The `+ 1` is the calling thread, which takes a lane itself.
+
+The caller, holding `mutex`:
+
+1. publishes the batch — context, jobs, results, `n_jobs` and `use`
+2. sets `outstanding = use - 1`
+3. bumps `generation`
+4. broadcasts on `work_ready`
+
+then releases the mutex and **runs lane 0 itself**, striding `i = 0; i < n_jobs; i += use`. This
+matters at these batch sizes: six jobs wakes five workers rather than six, and the caller does useful
+work instead of blocking. Finally it waits on `work_done` until `outstanding` reaches zero.
+
+Each worker parks on `work_ready` until `generation` changes, records the new generation
+unconditionally so it cannot spin, and participates only if its lane index is `< cur_use`. Workers
+outside that range park again without touching `outstanding`. A participating worker copies the batch
+pointers locally, releases the mutex, strides `i = lane; i < n_jobs; i += use`, then re-acquires the
+mutex and notifies `work_done` if it was the last one out.
+
+Workers simulate through `thread_scratch()` from stage 1, so a persistent worker allocates its
+buffers on its first batch and reuses them for the life of the process. Stage 1 replaced the scratch
+local in the per-call worker lambda; stage 2 replaces that lambda with a parked worker, and the
+accessor carries over unchanged.
+
+Nobody holds the mutex while simulating. The partition is the same stride the current code uses, and
+`run_batch_job` already only reads the const context and writes its own result slot — the property
+that makes today's threading safe is unchanged.
+
+### Bypass
+
+`n_threads <= 1 || n_jobs <= 1` runs inline exactly as today and never touches the pool, so
+`threads: 0`, `annual.py` and the test harness never allocate a worker.
+
+### Sizing
+
+`reserve_workers` grows to whatever `n_threads` asks for and never shrinks. A later, smaller request
+simply wakes fewer lanes.
+
+## Error handling
+
+**Fork.** Threads do not survive `fork()`, so a child would inherit a pool whose workers do not exist
+and hang waiting on them. Nothing in Predbat forks today — PR #4540 deleted the last thing that did —
+but `hass.py` still calls `set_start_method("fork")`, so a future feature that spawns a process would
+trip it. A `pthread_atfork` child handler sets the pool pointer to null: no allocation, no locking,
+async-signal-safe. The child lazily builds a fresh pool on its next batch, and the dead object leaks
+in the child's address space, which is standard and costs a few hundred bytes. Registered once via
+`std::call_once` before the pool is first created, guarded by `#if defined(__unix__) || defined(__APPLE__)`.
+
+**Concurrent callers.** Two Python threads can enter `pk_run_batch` at once, because ctypes releases
+the GIL. With one shared pool their published state would race, so a dispatch mutex is held for the
+whole batch and concurrent callers serialise. Today they would each spawn private threads and run in
+parallel, so this is a regression in a case nothing exercises; a data race is the worse option.
+
+**Thread creation failure.** `std::thread` throws `std::system_error` under resource exhaustion.
+`reserve_workers` catches it, keeps whatever workers were created, and `run` clamps `use` to
+`workers.size() + 1` — falling all the way back to inline if none exist. A home-automation daemon
+should degrade, not crash.
+
+**Shutdown.** The pool is never destroyed, deliberately. A static destructor running while CPython
+tears down risks a deadlock for no benefit; the OS reclaims the threads at exit.
+
+**Per-job failures** are unchanged: each job's `status` field still carries its own result, so one
+malformed scenario cannot discard the fan-out.
+
+## Testing
+
+Every new test is mutation-checked — break the thing deliberately, confirm the test fails, restore.
+That method found three real defects on PR #4540, two of them tests that could not fail.
+
+| Pins | Test |
+|---|---|
+| Stage 1's risk | Alternate two contexts with different `forecast_minutes` on one thread; each must match its single-context result. Stale tail data or a mis-sized buffer appears here and nowhere else. |
+| Worker reuse | Run the 1/2/4/8-thread parity sweep twice. The first batch creates workers, the second wakes parked ones — different paths, and "works once, breaks on reuse" is the classic pool bug. |
+| Lane arithmetic | `n_jobs < n_threads`: a 3-job batch at 16 threads, so `use = 3` and thirteen workers must park without touching `outstanding`. This is the median-6 production shape. |
+| Grow then wake fewer | Call at 8 threads, then 2, then 16. |
+| The dispatch mutex | Two Python threads calling `pk_run_batch` at once; results must match serial. |
+| The `pthread_atfork` handler | `os.fork()`, run a batch in the child, compare against the parent's result, check the child's exit status. Skipped where fork is unavailable. Without this the handler is unverified code whose whole job is preventing a hang. |
+
+Existing gates that must continue to hold: the `kernel_parity` sweeps, the 20-scenario byte-identical
+run (`+0.0000` on metric, cost and all three PV futures), and the full suite.
+
+## Measurement
+
+Interleaved, best of 3 per side, on one machine in one sitting — the method used for PR #4540's
+numbers.
+
+- **Stage 1** on the serial benchmark (`threads: 0`), which is where the 25,000 allocations are.
+- **Stage 2** on the threaded benchmark, with serial re-checked to confirm it has not regressed.
+
+Each stage is measured against the tree it modifies, so the two effects are attributable separately.
+If stage 1 recovers most of the available time, stage 2 can be dropped rather than accepting its
+concurrency risk for a small remainder.
+
+**Honest ceiling.** This turns a thread creation (tens of microseconds) into a wake (a few
+microseconds), so threading can pay where it currently cannot. But the upside is still bounded by the
+kernel's share of plan time, so single-digit to low-teens percent is the realistic expectation, not
+anything resembling a 16x.
+
+## Deliverables
+
+- `apps/predbat/prediction_kernel.cpp`: `thread_scratch()`, `PkThreadPool`, the atfork handler, and
+  `pk_run_batch` rewired onto them.
+- `apps/predbat/tests/test_kernel_parity.py`: the six tests above.
+- All six shipped binaries rebuilt with `build_kernel_cross.sh` and confirmed to load.
+- No change to any Python module.
+
+## What measurement changed
+
+Three parts of the design above did not survive contact with a benchmark. They are left in place so
+the reasoning can be compared against what actually happened.
+
+**Stage 1 is a no-op on its own.** Removing ~25,000 allocations of ~70KB per plan changed nothing:
+26.291s before, 26.344s after, interleaved best of 3 against ~1.8% run-to-run spread. The premise was
+a misreading of the kernel's own comment, which recorded the allocator being the bottleneck under
+four threads contending — a different problem from one thread repeatedly allocating and freeing the
+same-sized block, which never leaves the allocator's thread cache. Stage 1 is kept only because the
+pool's parked workers need per-thread scratch, and because its two tests are worth having.
+
+**Broadcasting to wake workers cost most of the win.** The design says "broadcasts on `work_ready`".
+Built that way the pool was worth 1.3% (25.833s → 25.489s), because a six-job batch woke all fifteen
+workers and eleven of them went straight back to sleep — roughly 250,000 wasted wakeups per plan.
+Giving each worker its own flag and condition variable, so exactly the needed lanes wake, took it to
+3.7% (26.008s → 25.056s). The shipped code does that; the single `work_ready` variable in the sketch
+above does not exist.
+
+**The `threads: auto` cap was measured and rejected.** With the pool in place the fast-machine curve
+peaks below the core count — serial 26.33s, 4 threads 24.89s, 6 threads 24.71s, 8 threads 24.91s,
+16 threads 25.04s — which argues for capping `auto`. Re-running with each job made eight times dearer,
+which is how a machine where the kernel dominates behaves, the curve stops turning over: 48.92s
+serial, 32.03s at 4, 29.98s at 6, 29.85s at 8, 28.94s at 16. Capping at 4 therefore costs 0.7% on a
+fast machine but 10.7% on a kernel-heavy one, while not capping costs 1.3% at worst. `auto` is left
+as the core count; `resolve_batch_threads` in `plan.py` carries the numbers.
+
+**Final result:** 26.33s serial → 24.71s at 6 threads on the 20-scenario benchmark, **6.1%**, on top
+of the 35% the batching work delivered. All 20 scenarios byte-identical at every thread count, in
+both the normal and the kernel-heavy regime.
+
+## What the tests found
+
+Every new test was mutation-checked, and two were not good enough on the first attempt:
+
+- The **fork** test earns its place. With the `pthread_atfork` handler removed the forked child
+  deadlocks on workers that no longer exist; the test kills it on a deadline and reports the timeout
+  rather than hanging the suite.
+- The **concurrency** test initially did not bite. With the dispatch mutex deleted, 25 rounds over 4
+  lanes passed happily — the GIL keeps two callers from overlapping often enough for the race to
+  land. At 400 rounds over 8 lanes it deadlocks within seconds. The round count is therefore
+  load-bearing and the test says so.
+- The **scratch cross-context** test cannot fail before its change, since without reuse there is
+  nothing to leak; it was verified by relaxing `build_window_membership`'s `assign` to skip shrinking,
+  which makes it fail immediately.