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Preferred Networks Releases CuPy v8

New major update to open-source library for general-purpose matrix calculation


TOKYOOctober 1, 2020 – Preferred Networks, Inc. (PFN) today released CuPy™ v8, the new major update to the open-source library for general-purpose matrix calculation.

CuPy v8 provides the following new features:

  • Support for CUDA 11 and the latest NVIDIA GPU (Ampere architecture)
    Boosts single-precision mathematics using TensorFloat-32 (TF32) computation mode
  • Official support for NVIDIA cuTENSOR/CUB
    Performance improvements up to 9.7x for matrix computations in our benchmarks (see blog post for details)
  • Enhanced kernel fusion
    Now supports merging computations including multiple reductions into a single kernel
  • Automatic tuning of kernel launch parameters using Optuna
    Discover the optimal launch parameters depending on the data properties to improve performance
  • Memory pool sharing with external libraries
    Improved interoperability with PyTorch by using pytorch-pfn-extras; for example, you can flexibly integrate CuPy as a preprocess code into the PyTorch workflow
  • Improved NumPy/SciPy function coverage
    Many functions added, including the NumPy Polynomials package (results of Google Summer of Code 2020) and the SciPy image processing package

PFN will continue to swiftly incorporate the latest research outcomes while collaborating with supporting companies and open source communities for the development of CuPy.


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