NVIDIA CuPyNumeric Allows Scientists To Use GPU Acceleration

The goal of NVIDIA cuPyNumeric is to provide distributed and accelerated computation on the NVIDIA platform for the Python community

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Scales and speeds up current NumPy workflows transparently offers NumPy a smooth drop-in substitute

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Requires few code modifications, enabling scientific activities to be completed more quickly and openly accessible

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Optimally scales from a single CPU to thousands of GPUs and Start using Conda or GitHub

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Offers automated acceleration and parallelism for several nodes spanning CPUs and GPUs

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They may execute their programs on one or hundreds of GPUs with no code modifications after applying cuPyNumeric

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cuPyNumeric reduced run time from minutes to seconds and six times sped their data analysis application

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