The Storage Formats for Accelerating SMVP on a GPU
Jin Wen Tian, Fei Qing Wu, Rui Zou, Guohui Zeng, Li Gong · 2013
This paper aims to study how to choose an effective storage format to accelerate sparse matrix vector product (SMVP) occurring in different numerical methods. We discuss and analyze the storage formats of SMVP which implemented on a GPU. The formats are used for hastening the solution of equations arising from numerical methods. The research in this paper can provide fast selects, which allow low storage space and make memory accesses efficiency, for numerical methods to accelerate SMVP.