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arXiv · 2105.13480

Efficient distributed algorithms for Convolutional Neural Networks

Rui Li·Yufan Xu·Aravind Sukumaran-Rajam·Atanas Rountev·P Sadayappan
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Abstract

Several efficient distributed algorithms have been developed for matrix-matrix multiplication: the 3D algorithm, the 2D SUMMA algorithm, and the 2.5D algorithm. Each of these algorithms was independently conceived and they trade-off memory needed per node and the inter-node data communication volume. The convolutional neural network (CNN) computation may be viewed as a generalization of matrix-multiplication combined with neighborhood stencil computations. We develop communication-efficient distributed-memory algorithms for CNNs that are analogous to the 2D/2.5D/3D algorithms for matrix-matrix multiplication.

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Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P Sadayappan. 2021-05-27. Efficient distributed algorithms for Convolutional Neural Networks. https://doi.org/10.1145/3409964.3461828

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