A Performance Prediction and Analysis Integrated Framework for SpMV on GPUs

Abstract

This paper presents unique modeling algorithms of performance prediction for sparse matrix-vector multiplication on GPUs. Based on the algorithms, we develop a framework that is able to predict SpMV kernel performance and to analyze the reported prediction results. We make the following contributions: (1) We provide theoretical basis for the generation of benchmark matrices according to the hardware features of a given specific GPU. (2) Given a sparse matrix, we propose a quantitative method to collect some features representing its matrix settings. (3) We propose four performance modeling algorithms to accurately predict kernel performance for SpMV computing using CSR, ELL, COO, and HYB SpMV kernels. We evaluate the accuracy of our framework with 8 widely-used sparse matrices (totally 32 test cases) on NVIDIA Tesla K80 GPU. In our experiments, the average performance differences between the predicted and measured SpMV kernel execution times for CSR, ELL, COO, and HYB SpMV kernels are 5.1%, 5.3%, 1.7%, and 6.1%, respectively.