抄録
Sparse matrix-vector multiplication (SpMV) represents the dominant cost in sparse linear algebra. However, sparse matrices exhibit inherent irregularity in both amount and distribution of none-zero values. This harnessed the tremendous potential of Single Instruction Multiple Data (SIMD) architectures, which is widely adopted in nowadays data-parallel processors. To improve the performance of SpMV, we proposed the Balanced SCT (B-SCT) method. The cornerstones are composed of the balanced-aware compression scheme and the on-the-fly data re-order structure. Our simulation results show that the B-SCT method provides an average speed-up of 130% over the commonly used CSR method, and 83% over the SIMD-oriented SCT method.
