CuPy

Last updated
CuPy
Original author(s) Seiya Tokui
Developer(s) Community, Preferred Networks, Inc.
Initial releaseSeptember 2, 2015;10 years ago (2015-09-02). [1]
Stable release
v13.3.0 [2] / August 22, 2024;12 months ago (2024-08-22) [2]
Repository github.com/cupy/cupy
Written in Python, Cython, CUDA
Operating system Linux, Windows
Platform Cross-platform
Type Numerical analysis
License MIT
Website cupy.dev

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]

Contents

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

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

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

SciPy-compatible APIs

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

User-defined GPU kernels

Distributed computing

Low-level CUDA features

Interoperability

Examples

Array creation

importcupyascpfromcupy.typingimportNDArrayx:NDArray[int]=cp.array([1,2,3])print(x)# prints array([1, 2, 3])y:NDArray[int]=cp.arange(10)print(y)# prints array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

Basic operations

importcupyascpfromcupy.typingimportNDArray,float32x:NDArray[float32]=cp.arange(12).reshape(3,4).astype(cp.float32)print(x)# prints: # array([[ 0.,  1.,  2.,  3.],#        [ 4.,  5.,  6.,  7.],#        [ 8.,  9., 10., 11.]], dtype=float32)print(x.sum(axis=1))# prints array([ 6., 22., 38.], dtype=float32)

Raw CUDA C/C++ kernel

importcupyascpfromcupy.driverimportIn,Out,RawKernelkern:RawKernel=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:In=cp.arange(16,dtype=cp.float32).reshape(4,4)in2:In=cp.arange(16,dtype=cp.float32).reshape(4,4)out:Out=cp.zeros((4,4),dtype=cp.float32)kern((4,),(4,),(in1,in2,out))# grid, block and argumentsprint(out)# prints:# array([[  0.,   1.,   4.,   9.],#        [ 16.,  25.,  36.,  49.],#        [ 64.,  81., 100., 121.],#        [144., 169., 196., 225.]], dtype=float32)

Applications

See also

References

  1. ↑ "Release v1.3.0 – chainer/chainer" . Retrieved 25 June 2022– via GitHub.
  2. 1 2 "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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