TITLE:
Performance Prediction Based on Statistics of Sparse Matrix-Vector Multiplication on GPUs
AUTHORS:
Ruixing Wang, Tongxiang Gu, Ming Li
KEYWORDS:
Sparse Matrix-Vector Multiplication, Performance Prediction, GPU, Normal Distribution, Uniform Distribution
JOURNAL NAME:
Journal of Computer and Communications,
Vol.5 No.6,
April
28,
2017
ABSTRACT: As one of the most essential and important operations in linear algebra, the performance prediction of sparse matrix-vector multiplication (SpMV) on GPUs has got more and more attention in recent years. In 2012, Guo and Wang put forward a new idea to predict the performance of SpMV on GPUs. However, they didn’t consider the matrix structure completely, so the execution time predicted by their model tends to be inaccurate for general sparse matrix. To address this problem, we proposed two new similar models, which take into account the structure of the matrices and make the performance prediction model more accurate. In addition, we predict the execution time of SpMV for CSR-V, CSR-S, ELL and JAD sparse matrix storage formats by the new models on the CUDA platform. Our experimental results show that the accuracy of prediction by our models is 1.69 times better than Guo and Wang’s model on average for most general matrices.