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Efficient Tiled Sparse Matrix Multiplication through Matrix Signatures

Abstract : Tiling is a key technique to reduce data movement in matrix computations. While tiling is well understood and widely used for dense matrix/tensor computations, effective tiling of sparse matrix computations remains a challenging problem. This paper proposes a novel method to efficiently summarize the impact of the sparsity structure of a matrix on achievable data reuse as a one-dimensional signature, which is then used to build an analytical cost model for tile size optimization for sparse matrix computations. The proposed model-driven approach to sparse tiling is evaluated on two key sparse matrix kernels: Sparse Matrix-Dense Matrix Multiplication (SpMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). Experimental results demonstrate that model-based tiled SpMM and SDDMM achieve high performance relative to the current state-of-the-art.
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Contributor : Fabrice Rastello Connect in order to contact the contributor
Submitted on : Thursday, January 21, 2021 - 11:27:54 AM
Last modification on : Thursday, January 20, 2022 - 5:26:29 PM
Long-term archiving on: : Thursday, April 22, 2021 - 6:59:06 PM


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  • HAL Id : hal-03117491, version 1



Süreyya Emre, Aravind Sukumaran-Rajam, Fabrice Rastello, Ponnuswamy Sadayyapan. Efficient Tiled Sparse Matrix Multiplication through Matrix Signatures. SC 2020 - International Conference for High Performance Computing, Networking, Storage and Analysis, Nov 2020, virtual, United States. pp.1-13. ⟨hal-03117491⟩



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