Is Sparse Matrix Reordering Effective for Sparse Matrix-Vector Multiplication?

TL;DR

This paper evaluates how sparse matrix reordering affects the performance of sparse matrix-vector multiplication on various multicore CPUs, highlighting potential performance gains and variability across hardware and strategies.

Contribution

It provides a comprehensive analysis of reordering strategies' effectiveness on different CPU architectures, considering both sequential and parallel execution impacts.

Findings

Reordering can significantly improve performance by reducing data movement.

Performance gains vary across different CPU architectures and reordering strategies.

Load imbalance and measurement methodology influence the observed benefits.

Abstract

This work evaluates the impact of sparse matrix reordering on the performance of sparse matrix-vector multiplication across different multicore CPU platforms. Reordering can significantly enhance performance by optimizing the non-zero element patterns to reduce total data movement and improve the load-balancing. We examine how these gains vary over different CPUs for different reordering strategies, focusing on both sequential and parallel execution. We address multiple aspects, including appropriate measurement methodology, comparison across different kinds of reordering strategies, consistency across machines, and impact of load imbalance.