DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication

著者: Yuechen Lu, Weifeng Liu - Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis 2023 被引用: 33

Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing, and machine learning applications. Much work on SpMV was devoted to resolving problems such as random access to the vector x and unbalanced load. However, we have experimentally found that the computation of inner products still occupies much overhead in the SpMV operation, which has been largely ignored in existing work.