Improving the Accuracy in SpMV Implementation Selection with Machine Learning

Reo Furuhata, Minglu Zhao, Mulya Agung, Ryusuke Egawa, Hiroyuki Takizawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Although there are various ways of implementing sparse matrix vector multiplication (SpMV), there is no established way of predicting the best implementation for individual sparse matrices, and thus an SpMV implementation has empirically been selected for each matrix. Cui et al. have proposed a machine learning approach to the prediction. However, their approach focuses only on predicting the best implementation for each matrix, and does not consider the performance differences among candidate implementations. If the performance difference between the best and non-best implementations for a matrix is large, the performance loss by the misprediction is also large. Thus, a machine learning model needs to be trained to preferentially avoid misprediction of such a matrix to achieve a higher expected performance. Therefore, this paper presents a machine learning approach that considers the performance differences at the best SpMV implementation selection problem and quantitatively discusses the performance improvement by the approach. The evaluation results clearly demonstrate that the proposed approach can prevent a machine learning model from selecting significantly low-performance implementations, and thereby improve the expected performance in comparison with the previous approach.

Original languageEnglish
Title of host publicationProceedings - 2020 8th International Symposium on Computing and Networking Workshops, CANDARW 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages172-177
Number of pages6
ISBN (Electronic)9781728199191
DOIs
Publication statusPublished - 2020 Nov
Event8th International Symposium on Computing and Networking Workshops, CANDARW 2020 - Virtual, Naha, Japan
Duration: 2020 Nov 242020 Nov 27

Publication series

NameProceedings - 2020 8th International Symposium on Computing and Networking Workshops, CANDARW 2020

Conference

Conference8th International Symposium on Computing and Networking Workshops, CANDARW 2020
Country/TerritoryJapan
CityVirtual, Naha
Period20/11/2420/11/27

Keywords

  • machine learning
  • sparse matrices
  • system implementation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Computational Mathematics
  • Control and Optimization

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