Accelerating Sparse General Matrix-Matrix Multiplication for NVIDIA Volta GPU and Hygon DCU
Author:
Affiliation:
1. Institute of Software Chinese Academy of Sciences, Beijing, China
Funder
China?s National Key Research and Development Project
GHfund
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3588195.3595936
Reference3 articles.
1. Tao Tang Xuejun Yang Yisong Lin 2011. Cache Miss Analysis for GPU Programs Based on Stack Distance Profile. ICDCS. 623--634. Tao Tang Xuejun Yang Yisong Lin 2011. Cache Miss Analysis for GPU Programs Based on Stack Distance Profile. ICDCS. 623--634.
2. Gonzalo Berger Manuel Freire Renzo Marini Ernesto Dufrechou Pablo Ezzatti 2021. Unleashing the performance of bmSparse for the sparse matrix multiplication in GPUs. ScalA@SC. 19--26. Gonzalo Berger Manuel Freire Renzo Marini Ernesto Dufrechou Pablo Ezzatti 2021. Unleashing the performance of bmSparse for the sparse matrix multiplication in GPUs. ScalA@SC. 19--26.
3. Akrem Benatia Weixing Ji Yizhuo Wang Feng Shi 2020. Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms. Int. J. High Perform. Comput. Appl. 34(1). Akrem Benatia Weixing Ji Yizhuo Wang Feng Shi 2020. Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms. Int. J. High Perform. Comput. Appl. 34(1).
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