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Hardware Details

GPU: Intel Arc Pro B70 Pro

Spec Value
Architecture Battlemage (Xe2-HPG)
Die BMG-G31 (full Big Battlemage)
Xe Cores 32
XMX Engines 256
Boost Clock 2800 MHz
Graphics Clock 2280 MHz (typical)
VRAM 32 GB GDDR6 ECC
Memory Bus 256-bit
Memory Bandwidth 608 GB/s (spec), 508 GB/s measured D2D
TDP 230 W
PCIe 5.0 x16
FP64 ~1.43 TFLOPS
FP32 ~22.94 TFLOPS
Release March 2026

PCIe Topology (Important!)

CPU PEG 00:06.0          → PCIe 5.0 ×16 ✅ (correct)
└─ On-Card Switch 02:00.0 → PCIe 5.0 ×16 ✅ (correct)
   └─ Switch DS 03:01.0   → PCIe 1.0 ×1  ← REPORTING BUG (xe driver)
      └─ B70 GPU 04:00.0

lspci reports PCIe 1.0 x1 — this is a known xe-driver bug in SR-IOV PF mode.

Real bandwidth measured via SYCL memcpy: ~15 GB/s H2D (consistent with PCIe 4.0 x8 due to dual-tile architecture splitting the link internally; PCIe 1.0 x1 would be 0.25 GB/s — 60× slower than measured).

See findings/01_pcie_reporting_bug.md.

Measured Hardware Performance

Test Result Theory Efficiency
GPU FP64 FMA 1374 GFLOPS 1430 GFLOPS 96%
GPU FP32 FMA 12364 GFLOPS 22940 GFLOPS 54%
VRAM Triad sustained (2 GiB) 531 GB/s 608 GB/s 87%
VRAM D2D Triad (4 MB) 508 GB/s 608 GB/s 83%
PCIe H2D (64MB) 15.5 GB/s ~50 GB/s 31%
RAM Triad (8 P-Cores) 77 GB/s 109 GB/s 71%
CPU DGEMM FP64 (8T MKL) 592 GFLOPS ~1500 GFLOPS 39%
CPU DGEMM FP64 (24T MKL) 1106 GFLOPS ~1500 GFLOPS 74%

GPU FP64 hits 96% of theoretical peak — the silicon is excellent for double-precision compute. The FP32 spec (22.94 TFLOPS) assumes XMX-style packed math; pure SIMD FMA gets 1× per ALU.

VRAM Triad ~530 GB/s is sustained from 64 MiB up to 4 GiB per array (12 GiB total touched). The 16 MiB outlier (1672 GB/s) is an SLC cache hit.

PCIe H2D Bandwidth vs Transfer Size

Transfer Size H2D GB/s D2H GB/s D2D GB/s Latency µs
1 MB 10.4 14.4 55.1 101
4 MB 12.3 12.6 508.0 341
16 MB 14.1 15.9 273.5 1191
64 MB 15.5 14.4 257.6 4338
256 MB 14.8 13.7 259.8 18105
1024 MB 9.9 6.1 259.0 108310

Key finding: PCIe bandwidth is NOT the bottleneck for our CFD case. Level Zero kernel launch latency NOT the bottleneck — measured at 5.6 µs sync / 1.5 µs async, on par with CUDA. See findings/14. The real bottleneck is PCIe host-buffer copies forced by lack of GPU-aware MPI in OGL, plus per-operation framework overhead in OGL+Ginkgo.