Mapping and Optimization Method of SpMV on Multi-DSP Accelerator

Abstract

Sparse matrix-vector multiplication (SpMV) solves the product of a sparse matrix and dense vector, and the sparseness of a sparse matrix is often more than 90%. Usually, the sparse matrix is compressed to save storage resources, but this causes irregular access to dense vectors in the algorithm, which takes a lot of time and degrades the SpMV performance of the system. In this study, we design a dedicated channel in the DMA to implement an indirect memory access process to speed up the SpMV operation. On this basis, we propose six SpMV algorithm schemes and map them to optimize the performance of SpMV. The results show that the M processor’s SpMV performance reached 6.88 GFLOPS. Besides, the average performance of the HPCG benchmark is 2.8 GFLOPS.