Understanding the Performances of Sparse Compression Formats Using Data Parallel Programming Model

Abstract

Several applications in numerical scientific computing involve very large sparse matrices with a regular or irregular sparse structure. These matrices can be stored using special compression formats (storing only non-zero elements) to reduce memory space and processing time. The choice of the optimal format is a critical process that involves several criteria. The general context of this work is to propose an auto-tuner system that, given a sparse matrix, a numerical method, a parallel programming model and an architecture, can automatically select the Optimal Compression Format (OCF). In this paper we study the performance of two different sparse compression formats namely CSR (Compressed Sparse Row) and CSC (Compressed Sparse Column). Thus, we propose data parallel algorithms for Sparse Matrix Vector Product in the case of each format. We extract a set of parameters that can help us to select the more suitable compression format for a given sparse matrix.