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Accelerating sparse matrix-matrix multiplication with GPU Tensor Cores
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DOI：10.1016/j.compeleceng.2020.106848.png)
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Sparse general matrix-matrix multiplication (spGEMM) is an essential component in many scientific and data analytics applications. However, the sparsity pattern of the input matrices and the interaction of their patterns make spGEMM challenging. Modern GPUs include Tensor Core Units (TCUs), which specialize in dense matrix multiplication. Our aim is to re-purpose TCUs for sparse matrices. The key idea of our spGEMM algorithm, tSparse, is to multiply sparse rectangular blocks using the mixed precision mode of TCUs. tSparse partitions the input matrices into files and operates only on files which contain one or more elements. It creates a task list of the files, and performs matrix multiplication of these files using TCUs. To the best of our knowledge, this is the first time that TCUs are used in the context of spGEMM. We show that spGEMM, with our filing approach, benefits from TCUs. Our approach significantly improves the performance of spGEMM in comparison to cuSPARSE, CUSP, RMerge2, Nsparse, AC-SpGEMM and spECK.
Keyword:
Sparse matrix multiplication
GPU
Tensor Cores
Parallel computing
SpGEMM
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A framework for general sparse matrix-matrix multiplication on GPUs and heterogeneous processors基于gpu和异构处理器的通用稀疏矩阵-矩阵乘法框架OAAI
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