Wikipedia

CuPy

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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.[3] 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.[4][5]

CuPy
Original author(s)Seiya Tokui
Developer(s)Community, Preferred Networks, Inc.
Initial releaseSeptember 2, 2015; 9 years ago (2015-09-02).[1]
Stable release
v13.3.0[2] / August 22, 2024; 3 months ago (2024-08-22)[2]
Repositorygithub.com/cupy/cupy
Written inPython, Cython, CUDA
Operating systemLinux, Windows
PlatformCross-platform
TypeNumerical analysis
LicenseMIT
Websitecupy.dev

CuPy has been initially developed as a backend of Chainer deep learning framework, and later established as an independent project in 2017.[6]

CuPy is a part of the NumPy ecosystem array libraries[7] and is widely adopted to utilize GPU with Python,[8] especially in high-performance computing environments such as Summit,[9] Perlmutter,[10] EULER,[11] and ABCI.[12]

CuPy is a NumFOCUS sponsored project.[13]

Features

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CuPy implements NumPy/SciPy-compatible APIs, as well as features to write user-defined GPU kernels or access low-level APIs.[14][15]

NumPy-compatible APIs

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

SciPy-compatible APIs

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

User-defined GPU kernels

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

Distributed computing

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

Low-level CUDA features

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  • Stream and event
  • Memory pool
  • Profiler
  • Host API binding
  • CUDA Python support[16]

Interoperability

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Examples

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Array creation

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>>> import cupy as cp
>>> x = cp.array([1, 2, 3])
>>> x
array([1, 2, 3])
>>> y = cp.arange(10)
>>> y
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

Basic operations

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>>> import cupy as cp
>>> x = cp.arange(12).reshape(3, 4).astype(cp.float32)
>>> x
array([[ 0.,  1.,  2.,  3.],
       [ 4.,  5.,  6.,  7.],
       [ 8.,  9., 10., 11.]], dtype=float32)
>>> x.sum(axis=1)
array([ 6., 22., 38.], dtype=float32)

Raw CUDA C/C++ kernel

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>>> import cupy as cp
>>> kern = cp.RawKernel(r'''
... extern "C" __global__
... void multiply_elemwise(const float* in1, const float* in2, float* out) {
...     int tid = blockDim.x * blockIdx.x + threadIdx.x;
...     out[tid] = in1[tid] * in2[tid];
... }
... ''', 'multiply_elemwise')
>>> in1 = cp.arange(16, dtype=cp.float32).reshape(4, 4)
>>> in2 = cp.arange(16, dtype=cp.float32).reshape(4, 4)
>>> out = cp.zeros((4, 4), dtype=cp.float32)
>>> kern((4,), (4,), (in1, in2, out))  # grid, block and arguments
>>> out
array([[  0.,   1.,   4.,   9.],
       [ 16.,  25.,  36.,  49.],
       [ 64.,  81., 100., 121.],
       [144., 169., 196., 225.]], dtype=float32)

Applications

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See also

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References

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  1. ^ "Release v1.3.0 – chainer/chainer". Retrieved 25 June 2022 – via GitHub.
  2. ^ a b "Releases – cupy/cupy". Retrieved 8 September 2024 – via GitHub.
  3. ^ Okuta, Ryosuke; Unno, Yuya; Nishino, Daisuke; Hido, Shohei; Loomis, Crissman (2017). CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations (PDF). Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS).
  4. ^ "CuPy 9.0 Brings AMD GPU Support To This Numpy-Compatible Library - Phoronix". Phoronix. 29 April 2021. Retrieved 21 June 2022.
  5. ^ "AMD Leads High Performance Computing Towards Exascale and Beyond". 28 June 2021. Retrieved 21 June 2022. Most recently, CuPy, an open-source array library with Python, has expanded its traditional GPU support with the introduction of version 9.0 that now offers support for the ROCm stack for GPU-accelerated computing.
  6. ^ "Preferred Networks released Version 2 of Chainer, an Open Source framework for Deep Learning - Preferred Networks, Inc". 2 June 2017. Retrieved 18 June 2022.
  7. ^ "NumPy". numpy.org. Retrieved 21 June 2022.
  8. ^ Gorelick, Micha; Ozsvald, Ian (April 2020). High Performance Python: Practical Performant Programming for Humans (2nd ed.). O'Reilly Media, Inc. p. 190. ISBN 9781492055020.
  9. ^ Oak Ridge Leadership Computing Facility. "Installing CuPy". OLCF User Documentation. Retrieved 21 June 2022.
  10. ^ National Energy Research Scientific Computing Center. "Using Python on Perlmutter". NERSC Documentation. Retrieved 21 June 2022.
  11. ^ ETH Zurich. "CuPy". ScientificComputing. Retrieved 21 June 2022.
  12. ^ National Institute of Advanced Industrial Science and Technology. "Chainer". ABCI 2.0 User Guide. Retrieved 21 June 2022.
  13. ^ "Sponsored Projects - NumFOCUS". Retrieved 8 September 2024.
  14. ^ "Overview". CuPy documentation. Retrieved 18 June 2022.
  15. ^ "Comparison Table". CuPy documentation. Retrieved 18 June 2022.
  16. ^ "CUDA Python | NVIDIA Developer". Retrieved 21 June 2022.
  17. ^ "Welcome to DLPack's documentation!". DLPack 0.6.0 documentation. Retrieved 21 June 2022.
  18. ^ "CUDA Array Interface (Version 3)". Numba 0.55.2+0.g2298ad618.dirty-py3.7-linux-x86_64.egg documentation. Retrieved 21 June 2022.
  19. ^ "NEP 13 — A mechanism for overriding Ufuncs — NumPy Enhancement Proposals". numpy.org. Retrieved 21 June 2022.
  20. ^ "NEP 18 — A dispatch mechanism for NumPy's high level array functions — NumPy Enhancement Proposals". numpy.org. Retrieved 21 June 2022.
  21. ^ Charles R Harris; K. Jarrod Millman; Stéfan J. van der Walt; et al. (16 September 2020). "Array programming with NumPy" (PDF). Nature. 585 (7825): 357–362. arXiv:2006.10256. doi:10.1038/S41586-020-2649-2. ISSN 1476-4687. PMC 7759461. PMID 32939066. Wikidata Q99413970.
  22. ^ "2021 report - Python Data APIs Consortium" (PDF). Retrieved 21 June 2022.
  23. ^ "Purpose and scope". Python array API standard 2021.12 documentation. Retrieved 21 June 2022.
  24. ^ "Install spaCy". spaCy Usage Documentation. Retrieved 21 June 2022.
  25. ^ Patel, Ankur A.; Arasanipalai, Ajay Uppili (May 2021). Applied Natural Language Processing in the Enterprise (1st ed.). O'Reilly Media, Inc. p. 68. ISBN 9781492062578.
  26. ^ "Python Package Introduction". xgboost 1.6.1 documentation. Retrieved 21 June 2022.
  27. ^ "UCBerkeleySETI/turbo_seti: turboSETI -- python based SETI search algorithm". GitHub. Retrieved 21 June 2022.
  28. ^ "Open GPU Data Science | RAPIDS". Retrieved 21 June 2022.
  29. ^ "API Docs". RAPIDS Docs. Retrieved 21 June 2022.
  30. ^ "Efficient Data Sharing between CuPy and RAPIDS". Retrieved 21 June 2022.
  31. ^ "10 Minutes to cuDF and CuPy". Retrieved 21 June 2022.
  32. ^ Alex, Rogozhnikov (2022). Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation. International Conference on Learning Representations.
  33. ^ "arogozhnikov/einops: Deep learning operations reinvented (for pytorch, tensorflow, jax and others)". GitHub. Retrieved 21 June 2022.
  34. ^ "Array API support (experimental) — scikit-learn documentation". Retrieved 8 September 2024.
  35. ^ Tokui, Seiya; Okuta, Ryosuke; Akiba, Takuya; Niitani, Yusuke; Ogawa, Toru; Saito, Shunta; Suzuki, Shuji; Uenishi, Kota; Vogel, Brian; Vincent, Hiroyuki Yamazaki (2019). Chainer: A Deep Learning Framework for Accelerating the Research Cycle. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3292500.3330756.
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