Overcoming Load Imbalance for Irregular Sparse Matrices
Workshop: IA^3 2017 - 7th Workshop on Irregular Applications: Architectures and Algorithms
Authors: Goran Flegar (Jaume I University)
Abstract: In this paper we propose a load-balanced GPU kernel for computing the sparse matrix vector (SpMV) product. Making heavy use of the latest GPU programming features, we also enable satisfying per formance for irregular and unbalanced matrices. In a performance comparison using 400 test matrices we reveal the new kernel being superior to the most popular SpMV implementations.