Optimizing partitioned CSR-based SpGEMM on the Sunway TaihuLight

Abstract

General sparse matrix-sparse matrix (SpGEMM) multiplication is one of the basic kernels in a great many applications. Several works focus on various optimizations for SpGEMM. To fully exploit the powerful computing capability of the Sunway TaihuLight supercomputer for SpGEMM, this paper designs the partitioning method and parallelization of CSR-based SpGEMM to make it well match to the Sunway architecture. In addition, this paper optimizes the partitioning method based on the distribution of the floating-point calculations of the CSR-based SpGEMM to achieve the load balance and performance improvement on the Sunway. We, respectively, analyze the performance, including the memory footprint and the execution time, of the parallel CSR-based SpGEMM and the optimized CSR-based SpGEMM on the Sunway. The experimental results show that the optimized CSR-based SpGEMM outperforms over the parallel CSR-based SpGEMM and has good scalability on the Sunway.