Threaded accurate matrix-matrix multiplications with sparse matrix-vector multiplications

Shuntaro Ichimura, Takahiro Katagiri, Katsuhisa Ozaki, Takeshi Ogita, Toru Nagai

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

3 Citations (Scopus)

Abstract

Basic Linear Algebra Subprograms (BLAS) is a frequently used numerical library for linear algebra computations. However, it places little emphasis on computational accuracy, especially with respect to the accuracy assurance of the results. Although some algorithms for ensuring the computational accuracy of BLAS operations have been studied, there is a need for performance evaluation in advanced computer architectures. In this study, we parallelize high-precision matrix-matrix multiplication using thread-level parallelism. In addition, we conduct a performance evaluation from the viewpoints of execution speed and accuracy. We implement a method to convert dense matrices into sparse matrices by exploiting the nature of the target algorithm and adapting sparse-vector multiplication. Results obtained using the FX100 supercomputer system at Nagoya University indicate that (1) implementation with the ELL format achieves 1.43x speedup and (2) a maximum of 38x speedup compared to conventional implementation for dense matrix operations with dgemm.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1093-1102
Number of pages10
ISBN (Print)9781538655559
DOIs
Publication statusPublished - 2018 Aug 3
Externally publishedYes
Event32nd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018 - Vancouver, Canada
Duration: 2018 May 212018 May 25

Other

Other32nd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018
CountryCanada
CityVancouver
Period18/5/2118/5/25

Keywords

  • Accuracy Assurance
  • Component
  • Error-free Transformation
  • High-precision Matrix-Matrix Multiplications
  • Sparse Matrix-vector Multiplications
  • Thread Parallelism

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

Fingerprint Dive into the research topics of 'Threaded accurate matrix-matrix multiplications with sparse matrix-vector multiplications'. Together they form a unique fingerprint.

  • Cite this

    Ichimura, S., Katagiri, T., Ozaki, K., Ogita, T., & Nagai, T. (2018). Threaded accurate matrix-matrix multiplications with sparse matrix-vector multiplications. In Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018 (pp. 1093-1102). [8425535] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPSW.2018.00168