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 are available under package.

SciPy-compatible APIs

The same set of APIs defined in the SciPy package are available under package.

User-defined GPU kernels

  • Kernel templates for element-wise and reduction operations
  • Raw kernel
  • Just-in-time transpiler
  • Kernel fusion

Distributed computing

  • Distributed communication package, providing collective and peer-to-peer primitives

Low-level CUDA features

Interoperability

Examples

Array creation


>>> import cupy as cp
>>> x = cp.array
>>> x
array
>>> y = cp.arange
>>> y
array

Basic operations


>>> import cupy as cp
>>> x = cp.arange.reshape.astype
>>> x
array
>>> x.sum
array

Raw CUDA C/C++ kernel


>>> import cupy as cp
>>> kern = cp.RawKernel
>>> in1 = cp.arange.reshape
>>> in2 = cp.arange.reshape
>>> out = cp.zeros
>>> kern,, ) # grid, block and arguments
>>> out
array

Applications