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2021 | OriginalPaper | Buchkapitel

A Dynamic Parameter Tuning Method for High Performance SpMM

verfasst von : Bin Qi, Kazuhiko Komatsu, Masayuki Sato, Hiroaki Kobayashi

Erschienen in: Parallel and Distributed Computing, Applications and Technologies

Verlag: Springer International Publishing

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Sparse matrix-matrix multiplication (SpMM) is a basic kernel that is used by many algorithms. Several researches focus on various optimizations for SpMM parallel execution. However, a division of a task for parallelization is not well considered yet. Generally, a matrix is equally divided into blocks for processes even though the sparsities of input matrices are different. The parameter that divides a task into multiple processes for parallelization is fixed. As a result, load imbalance among the processes occurs. To balance the loads among the processes, this paper proposes a dynamic parameter tuning method by analyzing the sparsities of input matrices. The experimental results show that the proposed method improves the performance of SpMM for examined matrices by up to 39.5% and 12.3% on average.

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Titel: A Dynamic Parameter Tuning Method for High Performance SpMM
verfasst von: Bin Qi
Kazuhiko Komatsu
Masayuki Sato
Hiroaki Kobayashi
Verlag: Springer International Publishing
Buch: Parallel and Distributed Computing, Applications and Technologies
Print ISBN: 978-3-030-69243-8

Electronic ISBN: 978-3-030-69244-5

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-69244-5_28

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