Python Frameworks for Developing HPC Applications

This repository includes a list of 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
• CUTLASS - CUDA Templates and Python DSLs for High-Performance Linear Algebra
• Dask - Easy parallel Python that does what you need
• DaCe - Data Centric Parallel Programming
• FlexFlow - Drop-in PyTorch, Keras, ONNX interface
• JAX - Composable transformations of Python+NumPy programs
• 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
• Triton - Language and compiler for writing high-performance custom kernels that run on GPUs

Contact

If you would like to make any addition, feel free to create a PR with suggested changes or email Milos Gligoric gligoric@utexas.edu.