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

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
CountrySingapore
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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  • Cite this

    Ohshima, S., Yamazaki, I., Ida, A., & Yokota, R. (2019). Optimization of numerous small dense-matrix-vector multiplications in h-matrix arithmetic on gpu. In Proceedings - 2019 IEEE 13th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2019 (pp. 9-16). [8906754] (Proceedings - 2019 IEEE 13th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MCSoC.2019.00009