Large sparse matrices with compound entries, i.e. complex and quaternionic matrices as well as matrices with dense blocks, are a core component of many algorithms in geometry processing, physically based animation and other areas of computer graphics. We generalize several matrix layouts and apply joint schedule and layout autotuning to improve the performance of the sparse matrix-vector product on massively parallel graphics processing units. Compared to schedule tuning without layout tuning, we achieve speedups of up to 5.5 ×. In comparison to cuSPARSE, we achieve speedups of up to 4.7 ×.
- parallel computing
- sparse matrix
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
Fields of Expertise
- Information, Communication & Computing