@inproceedings{okuta_cupy_2017,
	title = {{CuPy}: {A} {NumPy}-{Compatible} {Library} for {NVIDIA} {GPU} {Calculations}},
	url = {http://learningsys.org/nips17/assets/papers/paper_16.pdf},
	abstract = {CuPy 1 is an open-source library with NumPy syntax that increases speed by doing matrix operations on NVIDIA GPUs. It is accelerated with the CUDA platform from NVIDIA and also uses CUDA-related libraries, including cuBLAS, cuDNN, cuRAND, cuSOLVER, cuSPARSE, and NCCL, to make full use of the GPU architecture. CuPy’s interface is highly compatible with NumPy; in most cases it can be used as a drop-in replacement. CuPy supports various methods, data types, indexing, broadcasting, and more.},
	language = {en},
	booktitle = {Proceedings of {Workshop} on {Machine} {Learning} {Systems} ({LearningSys}) in {The} {Thirty}-first {Annual} {Conference} on {Neural} {Information} {Processing} {Systems} ({NIPS})},
	author = {Okuta, Ryosuke and Unno, Yuya and Nishino, Daisuke and Hido, Shohei and Loomis, Crissman},
	year = {2017},
	keywords = {\#Deep Learning, /unread},
}

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