| Popis: |
Graphics processing units (GPUs) have been used in the general-purpose computation field. Sparse matrix–vector multiplication (SpMV) algorithm is one of the most important scientific computing kernel algorithms. In this paper, we discuss implementing optimizing sparse matrix–vector multiplication on GPUs using CUDA programming model. We used methods and strategy which including mapping thread, merging access, reusing data, and avoiding the branch. The experimental results show that the optimizations strategy to improve SpMV performance. |
