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Can Python Do for HPC What It Did for Machine Learning?

This repository accopanies a Birds of a Feather session at Supercomputing Conference 2024.

Can Python Do for HPC What It Did for Machine Learning?
Wednesday, 20 November 2024 12:15pm - 1:15pm EST
Location B212

Description

Python is now one of the most popular programming languages. In HPC, it has predominantly been used to coordinate coarse-grain library components or workflows. However, it is increasingly being used to develop and coordinate applications with dynamic finer-grain components that are challenging to map efficiently onto heterogeneous resources. In this BoF, we discuss this challenge and efforts to design Python-based HPC, production quality codes for HPC leadership platforms. We will discuss issues such as multithreading, GPU kernel development, task-based coordination on heterogeneous systems with a mix of CPUs and GPUs, inter-node interoperability, scalability, portability, and reproducibility.

Program

Frameworks for HPC Python development

Below is an incomplete list of framework for developing HPC applications in Python and brief descriptions.

  • Arkouda - A numpy/pandas inspired Python library backed by Chapel
  • Charm4py - Charm++ programming model in Python
  • CuPy - NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python
  • cuPyNumeric - Write NumPy, run automatically on clusters of CPUs and GPUs
  • Dask - Easy parallel Python that does what you need
  • DaCe - Data Centric Parallel Programming
  • FlexFlow - Drop-in PyTorch, Keras, ONNX interface
  • lbmpy - Run fast fluid simulations based on the lattice Boltzmann method in Python on CPUs and GPUs
  • loopy - A code generator for array-based code in the OpenCL/CUDA execution model
  • mpi4py - MPI for Python
  • Numba - JIT compiler that translates a subset of Python and NumPy code into fast machine code
  • Pallas - An extension to JAX that enables writing custom kernels for GPU and TPU
  • Parla - A task-parallel programming library for Python
  • Parsl - Productive parallel programming in Python
  • PyCOMPS - Workflow orchestration in Python
  • PyCUDA - Pythonic access to Nvidia's CUDA parallel computation API
  • Pygion - A task-based framework for Python based on the Legion programming system
  • PyKokkos - Framework for writing performance portable HPC kernels in Python
  • PyOMP - OpenMP for Python in Numba for CPU/GPU parallel programming
  • PyOpenCL - Lets you access GPUs and other massively parallel compute devices from Python
  • pystencils - Run blazingly fast stencil codes on numpy arrays
  • PyTorch - An open-source machine learning library based on the Torch library
  • Ray - AI Compute Engine
  • Taichi Lang - Imperative, parallel programming language for high-performance numerical computation

Contact

If you would like to make any addition, feel free to create a PR with suggested changes or email Milos Gligoric [email protected].