CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of cupy. ndarray, the core multi-dimensional array class, and many functions on it. It supports a subset of numpy. ndarray interface. The following is a brief overview of supported subset of NumPy interface: • Basic indexing (indexing by ints, slices, newaxes, and Ellipsis) • Most of Advanced indexing (except for some indexing patterns with boolean masks) • Data types (dtypes): bool_, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32, float64, complex64, complex128 • Most of the array creation routines (empty, ones_like, diag, etc.) • Most of the array manipulation routines (reshape, rollaxis, concatenate, etc.) • All operators with broadcasting • All universal functions for elementwise operations (except those for complex numbers). • Linear algebra functions, including product (dot, matmul, etc.) and decomposition (cholesky, svd, etc.), accelerated by cuBLAS. • Reduction along axes (sum, max, argmax, etc.) CuPy also includes the following features for performance: • User-defined elementwise CUDA kernels • User-defined reduction CUDA kernels • Fusing CUDA kernels to optimize user-defined calculation • Customizable memory allocator and memory pool • cuDNN utilities
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