tutorialslist.yml

- title: Accelerating and Scaling Python for HPC (SC26)
  author: "Vassil Vassilev (Princeton/CERN)"
  abstract: |
    Full-day hands-on SC26 tutorial on writing, debugging, profiling, and
    optimizing multi-node GPU applications in Python: CuPy for drop-in GPU
    acceleration, Numba for custom kernels, mpi4py for multi-node scaling,
    and DragonHPC for workflow acceleration -- taught through guided Jupyter
    notebooks in a web-based environment (just a laptop and a browser).
  artifacts: |
    [Tutorial landing page](/tutorials/sc26/)
  url: "/tutorials/sc26/"
  date: 2026-11-16

- title: Accelerating and Scaling Python for HPC (ISC 2026)
  author: "Vassil Vassilev (Princeton/CERN)"
  abstract: |
    Hands-on tutorial at ISC High Performance 2026 (Hamburg) on writing,
    debugging, profiling, and optimizing multi-node GPU applications in
    Python: CuPy for drop-in GPU acceleration, Numba for custom kernels,
    and mpi4py for multi-node scaling, taught through guided Jupyter
    notebooks.
  artifacts: |
    [Tutorial archive page](/tutorials/isc26/)
  url: "/tutorials/isc26/"
  date: 2026-06-22

- title: Efficient Python Programming
  author: "Aaron Jomy (CERN); Vassil Vassilev (Princeton/CERN)"
  abstract: |

  artifacts: |
    [Link to Slides](/assets/presentations/Aaron_J-Efficient-Python-Programming.pdf)
    
  url: "/tutorials/efficient-python-programming/"
  date: 2024-08-20

- title: Building Programming Language Infrastructure With LLVM Components
  author: "Sunho Kim (De Anza College, Cupertino); Lang Hames (Apple); Vassil Vassilev (Princeton/CERN)"
  abstract: |
    LLVM and Clang are one of the foundational instruments for building research
    and production programming languages. The libraries are efficient and
    composable providing many opportunities to build research language
    infrastructure. The tutorial is organized into three major parts:

      1. Introduction, design principles, library layering (~45m) -- Provides an
        overview of LLVM and Clang.

      2. Just-in-Time Infrastructure (~60m) -- Introduces new capabilities of
        ORCv2 such as speculative JITing and re-optimization using a simple
        language called Kaleidoscope.

      3. Incremental compilation apt for dynamic programming languages (~45m) --
        Outlines how to use Clang as a library to enable build basic
        C/C++/Python on-demand by building a C++ interpreter which connects to
        the Python interpreter. Video available [here](https://youtu.be/Rvl1QitGWuM).

    Upon completion of the tutorials researchers learn how to set up various
    LLVM components and use them to quickly bootstrap their research projects.

  url: "https://github.com/compiler-research/pldi-tutorials-2023"
  date: 2023-07-17

- title: How to Execute Gradients Generated by Clad on a CUDA GPU
  author: Ioana Ifrim
  abstract: |
    CLAD provides automatic differentiation (AD) for C/C++ and works without
    code modification (legacy code). Given that the range of AD application
    problems are defined by their high computational requirements, it means
    that they can greatly benefit from parallel implementations on graphics
    processing units (GPUs).
    This tutorial showcases how to firstly use CLAD to obtain your function’s
    gradient and how to schedule the function’s execution on the GPU.

  url: /tutorials/clad_cuda_simple/
  date: 2021-08-20

- title: Interactive Automatic Differentiation With Clad and Jupyter Notebooks
  author: Ioana Ifrim
  abstract: |
    xeus-cling provides a Jupyter kernel for C++ with the help of the C++
    interpreter cling and the native implementation of the Jupyter protocol
    xeus. Within the xeus-cling framework, CLAD can enable automatic
    differentiation (AD) such that users can automatically generate C++ code
    for their computation of derivatives of their functions. In mathematical
    optimization, the Rosenbrock function is a non-convex function used as a
    performance test problem for optimization problems, this tutorial shows the
    computation of the function’s derivatives, by employing either CLAD’s
    Forward Mode or Reverse Mode.

  url: /tutorials/clad_jupyter/
  date: 2021-08-20

- title: Floating-Point Error Estimation Using Automatic Differentiation with Clad
  author: Garima Singh
  abstract: |
    CLAD provides an in-built floating-point error estimation framework that
    can automatically annotate code with error estimation information. This
    framework also provides the ability for users to write their own error models
    and use the same for generating error estimates. The aim of this tutorial is 
    to demonstrate building a simple custom error model and using it in conjunction
    with CLAD's error estimation framework.

  url: /tutorials/fp_error_estimation_clad_tutorial/
  date: 2021-08-21

- title: Game of Life on GPU Using Cling-CUDA
  author: Simeon Ehrig
  abstract: |
    This tutorial demonstrates some functions of Cling-CUDA and Jupyter Notebooks
    and gives an idea what you can do with C++ in a web browser. The example
    shows the usual workflow of simulation and analysis. The simulation runs
    Conway's Game of Life on a GPU.

  url: /tutorials/gol_cling_cuda_jupyter/
  date: 2021-11-09