CuPy: NumPy and SciPy for GPU

1. cupy

   1 24 9,890 9.9 Python

   NumPy & SciPy for GPU

   

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3. scikit-learn-intelex

   2 4 1,251 9.7 Python

   Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

   

   There is a bit similar project which supports Intel GPU offloading: https://github.com/intel/scikit-learn-intelex

4. Killed by Google

   3 2,330 2,530 5.0 TypeScript

   Part guillotine, part graveyard for Google's doomed apps, services, and hardware.

   

   - (For those familiar with NumPy) CuPy is closer to NumPy than jax.numpy

   But CuPy does not support automatic gradient computation, so if you do deep learning, use JAX instead. Or PyTorch, if you do not trust Google to maintain a project for a prolonged period of time https://killedbygoogle.com/

5. Numba

   4 127 10,190 9.8 Python

   NumPy aware dynamic Python compiler using LLVM

   

   I'm surprised to see pytorch and Jax mentioned as alternatives but not numba : https://github.com/numba/numba

   I've recently had to implement a few kernels to lower the memory footprint and runtime of some pytorch function : it's been really nice because numba kernels have type hints support (as opposed to raw cupy kernels).

6. cuda-api-wrappers

   5 15 818 8.6 C++

   Thin C++-flavored header-only wrappers for core CUDA APIs: Runtime, Driver, NVRTC, NVTX.

   

   > probably the easiest way to interface with custom CUDA kernels

   In Python? Perhaps. Generally? No, it isn't. Full power of the CUDA APIs including all runtime compilation options etc. : https://github.com/eyalroz/cuda-api-wrappers/

   Example:

     // the source could be a string literal, loaded from a .cu file, etc.

7. pyamg

   6 1 587 8.6 Python

   Algebraic Multigrid Solvers in Python

   

   For my tasks, I had some success with algebraic multigrid solvers as preconditioner, for example from AMGCL or PyAMG. They are also reasonably easy to get started with.

   https://github.com/pyamg/pyamg

   https://github.com/ddemidov/amgcl

   But I only have to deal with positive definite systems, so YMMV.

   I am not sure whether those libraries can deal with multiple right-hand sides, but most complexity is in the preconditioners anyway.

8. amgcl

   7 2 767 4.1 C++

   C++ library for solving large sparse linear systems with algebraic multigrid method

   

   For my tasks, I had some success with algebraic multigrid solvers as preconditioner, for example from AMGCL or PyAMG. They are also reasonably easy to get started with.

   https://github.com/pyamg/pyamg

   https://github.com/ddemidov/amgcl

   But I only have to deal with positive definite systems, so YMMV.

   I am not sure whether those libraries can deal with multiple right-hand sides, but most complexity is in the preconditioners anyway.

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10. cupynumeric

    8 10 824 3.9 Python

    An Aspiring Drop-In Replacement for NumPy at Scale

    

    If you like cupy, definitely checkout the Multinode Multi-gpu version, cuNumeric: https://github.com/nv-legate/cunumeric

    Would love to get any feedback from the community.

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