A load balancing strategy for parallel computation of sparse permanents

TL;DR

This paper proposes a load balancing strategy for parallel computation of sparse matrix permanents, utilizing approximate permanent values to optimize job scheduling and significantly improve efficiency.

Contribution

It introduces a novel load balancing method based on approximating permanents to enhance parallel efficiency in computing sparse matrix permanents.

Findings

Parallel efficiency improved for fullerene graph permanents

Approximate permanent values effectively guide job scheduling

Method shows significant performance gains in nanoscience applications

Abstract

The research in parallel machine scheduling in combinatorial optimization suggests that the desirable parallel efficiency could be achieved when the jobs are sorted in the non-increasing order of processing times. In this paper, we find that the time spending for computing the permanent of a sparse matrix by hybrid algorithm is strongly correlated to its permanent value. A strategy is introduced to improve a parallel algorithm for sparse permanent. Methods for approximating permanents, which have been studied extensively, are used to approximate the permanent values of sub-matrices to decide the processing order of jobs. This gives an improved load balancing method. Numerical results show that the parallel efficiency is improved remarkably for the permanents of fullerene graphs, which are of great interests in nanoscience.