期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2016
卷号：9
期号：10
页码：99-106
出版社：SERSC
摘要：GPU is the suitable equipment for accelerating computing-intensive applications in order to get the higher throughput for High Performance Computing (HPC). Sparse Matrix-Vector Multiplication (SpMV) is the core algorithm of HPC, so the SpMV’s throughput on GPU may affect the throughput on HPC platform. In the paper, we focus on the latency of reduction routine in SpMV included in CUSP, such as accessing shared memory and bank conflicting while multiple threads simultaneously accessing the same bank. We provide shuffle method to reduce the partial results instead of reducing in the shared memory in order to improve the throughput of SpMV on Kepler GPU. Experiments show that shuffle method can improve the throughput up to 9% of the original routine of SpMV in CUSP on average.
