Parallel huge matrix multiplication on a cluster with GPGPU accelerators

Seungyo Ryu, Dong Seung Kim

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

3 Citations (Scopus)

Abstract

We design a parallel huge matrix multiplication algorithm on a cluster of GPU nodes. Since input matrices are too big to accommodate in the memory, the algorithm repeats the loading, computing, storing partial matrix data from/to disk and GPU buffer. The key to achieve the best speedup is not only to use GPU with full performance, but to reduce the overhead in data movement between disk and GPU buffer. We devise an efficient way to lower the latency of supplying the matching pair of the partial matrices to the GPU buffer, and to optimize the data partition, distribution, and disk access using the pipelined way. Experimental results show our algorithm outperforms a generic algorithm, resulting in the computing time reduction by 45%. Also, the scalability of the algorithm enhances with more GPU nodes.

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.
Pages877-882
Number of pages6
ISBN (Print)9781538655559
DOIs
Publication statusPublished - 2018 Aug 3
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
Country/TerritoryCanada
CityVancouver
Period18/5/2118/5/25

Keywords

  • GPU computing
  • Matrix multiplication
  • MPI
  • Parallel computing

ASJC Scopus subject areas

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

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