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  • Source: CuPy

    • CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them.
    CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU. CuPy supports Nvidia CUDA GPU platform, and AMD ROCm GPU platform starting in v9.0.
    CuPy has been initially developed as a backend of Chainer deep learning framework, and later established as an independent project in 2017.
    CuPy is a part of the NumPy ecosystem array libraries and is widely adopted to utilize GPU with Python, especially in high-performance computing environments such as Summit, Perlmutter, EULER, and ABCI.
    CuPy is a NumFOCUS sponsored project.


    Features


    CuPy implements NumPy/SciPy-compatible APIs, as well as features to write user-defined GPU kernels or access low-level APIs.


    = NumPy-compatible APIs

    =
    The same set of APIs defined in the NumPy package (numpy.*) are available under cupy.* package.

    Multi-dimensional array (cupy.ndarray) for boolean, integer, float, and complex data types
    Module-level functions
    Linear algebra functions
    Fast Fourier transform
    Random number generator


    = SciPy-compatible APIs

    =
    The same set of APIs defined in the SciPy package (scipy.*) are available under cupyx.scipy.* package.

    Sparse matrices (cupyx.scipy.sparse.*_matrix) of CSR, COO, CSC, and DIA format
    Discrete Fourier transform
    Advanced linear algebra
    Multidimensional image processing
    Sparse linear algebra
    Special functions
    Signal processing
    Statistical functions


    = User-defined GPU kernels

    =
    Kernel templates for element-wise and reduction operations
    Raw kernel (CUDA C/C++)
    Just-in-time transpiler (JIT)
    Kernel fusion


    = Distributed computing

    =
    Distributed communication package (cupyx.distributed), providing collective and peer-to-peer primitives


    = Low-level CUDA features

    =
    Stream and event
    Memory pool
    Profiler
    Host API binding
    CUDA Python support


    = Interoperability

    =
    DLPack
    CUDA Array Interface
    NEP 13 (__array_ufunc__)
    NEP 18 (__array_function__)
    Array API Standard


    Examples




    = Array creation

    =


    = Basic operations

    =


    = Raw CUDA C/C++ kernel

    =


    Applications


    spaCy
    XGBoost
    turboSETI (Berkeley SETI)
    NVIDIA RAPIDS
    einops
    scikit-learn
    MONAI
    Chainer


    See also



    Array programming
    List of numerical-analysis software
    Dask


    References




    External links


    Official website
    cupy on GitHub

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