Selection of Supervised Learning-based Sparse Matrix Reordering Algorithms

TL;DR

This paper presents a supervised learning model that intelligently selects the best sparse matrix reordering algorithm based on matrix features, significantly reducing solution time.

Contribution

Introduces a supervised learning approach for algorithm selection in sparse matrix reordering, improving efficiency over traditional empirical methods.

Findings

Achieves 55.37% reduction in solution time

Predicts optimal reordering algorithms accurately

Provides an average speedup ratio of 1.45

Abstract

Sparse matrix ordering is a vital optimization technique often employed for solving large-scale sparse matrices. Its goal is to minimize the matrix bandwidth by reorganizing its rows and columns, thus enhancing efficiency. Conventional methods for algorithm selection usually depend on brute-force search or empirical knowledge, lacking the ability to adjust to diverse sparse matrix structures.As a result, we have introduced a supervised learning-based model for choosing sparse matrix reordering algorithms. This model grasps the correlation between matrix characteristics and commonly utilized reordering algorithms, facilitating the automated and intelligent selection of the suitable sparse matrix reordering algorithm. Experiments conducted on the Florida sparse matrix dataset reveal that our model can accurately predict the optimal reordering algorithm for various matrices, leading to a 55.37% reduction in solution time compared to solely using the AMD reordering algorithm, with an average speedup ratio of 1.45.