Optimization of numerous small dense-matrix-vector multiplications in h-matrix arithmetic on gpu

Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota

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

    1 Citation (Scopus)

    Abstract

    Dense-matrix-vector multiplication is one of the well-known important matrix calculations. This calculation is provided a general matrix-vector multiplication (GEMV) function in the basic linear algebra subprograms (BLAS) libraries for several computation hardware. Traditionally, studies focus one large dense-matrix (the length of each side of the dense matrix is long)-vector multiplication. However, some applications require acceleration of numerous small dense-matrix-vector multiplications. This feature is provided by batched BLAS libraries. This calculation is also needed to compute a hierarchical-matrix-vector multiplication. In this study, we implemented numerous small dense-matrix-vector multiplications on a Pascal GPU and evaluated the performance. Thus, we considered the impact of optimization parameters and succeeded in obtaining a better performance than previous works. The maximum differences from our previous work is 28.47% and from batched GEMV of MAGMA BLAS is upto 81.81%. Moreover, we considered the use of two optimization parameters in one GPU kernel; one parameter was applied to some matrices, whereas the second parameter was applied to other matrices. The amount of the improvement was limited (upto 5%), a performance improvement was achieved. Our result will serve as a good reference for users who need to use numerous small dense-matrix-vector multiplications on a GPU and want to optimize a matrix-vector multiplication by hand-Tuning and auto-Tuning.

    Original languageEnglish
    Title of host publicationProceedings - 2019 IEEE 13th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages9-16
    Number of pages8
    ISBN (Electronic)9781728148823
    DOIs
    Publication statusPublished - Oct 2019
    Event13th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2019 - Singapore, Singapore
    Duration: Oct 1 2019Oct 4 2019

    Publication series

    NameProceedings - 2019 IEEE 13th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2019

    Conference

    Conference13th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2019
    Country/TerritorySingapore
    CitySingapore
    Period10/1/1910/4/19

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications
    • Hardware and Architecture
    • Electrical and Electronic Engineering
    • Control and Optimization

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