Transplantation and optimization of SpMV algorithm based on DCU accelerator

Abstract

In order to give full play to the advantages of DCU accelerator and solve the problems of algorithm SpMV(Sparse matrix-vector multiplication) with limited bandwidth, unbalanced load, and non-combined memory access, a SCSR(Static Compressed Sparse Row) using CSR(Compressed Sparse Row) storage format is proposed based on DCU accelerator. The algorithm statically allocates the same number of rows to each thread block according to the average number of non-zero elements in each row to avoid unnecessary computations; the application of storage resources is reduced by reusing the on-chip high-speed storage space LDS(Local Data Shared), thus improving the CU(Compute Unit) occupancy. The experiment uses 15 sparse matrices in different fields for testing. The results show that compared with the SpMV algorithm in the hipSPARSE library, the SCSR algorithm achieves an average speedup ratio of 4.83 times.