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Conference papers

Sequences of Sparse Matrix-Vector Multiplication on Fugaku’s A64FX processors

Abstract : We implement parallel and distributed versions of the sparse matrix-vector product and the sequence of matrixvector product operations, using OpenMP, MPI, and the ARM SVE intrinsic functions, for different matrix storage formats. We investigate the efficiency of these implementations on one and two A64FX processors, using a variety of sparse matrices as input. The matrices have different properties in size, sparsity and regularity. We observe that a parallel and distributed implementation shows good scaling on two nodes for cases where the matrix is close to a diagonal matrix, but the performances degrade quickly with variations to the sparsity or regularity of the input.
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https://hal.archives-ouvertes.fr/hal-03450283
Contributor : Jérôme Bruno Félix Jean Gurhem Connect in order to contact the contributor
Submitted on : Thursday, November 25, 2021 - 9:35:52 PM
Last modification on : Wednesday, May 11, 2022 - 12:36:04 PM
Long-term archiving on: : Saturday, February 26, 2022 - 10:04:34 PM

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Jérôme Gurhem, Maxence Vandromme, Miwako Tsuji, Serge Petiton, Mitsuhisa Sato. Sequences of Sparse Matrix-Vector Multiplication on Fugaku’s A64FX processors. CLUSTER 2021 - IEEE International Conference on Cluster Computing, Sep 2021, Portland, United States. pp.751-758, ⟨10.1109/Cluster48925.2021.00111⟩. ⟨hal-03450283⟩

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