November 2022

Conference Paper

SparseLU, A Novel Algorithm and Math Library for Sparse LU Factorization

By:
Valero Lara, Pedro ; Greenwalt, Cameron; Vetter, Jeffrey S
Page Number:
25-31
Book Title:
2022 IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms (IA3)
Publication Date:
November 2022
Publisher Location:
IEEE, New Jersey, United States of America
Conference Name:
2022 IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms (IA3)
Conference Location:
Dallas, Texas, United States of America
Conference Sponsor:
The International Conference for High Performance Computing, Networking, Storage, and Analysis
View DOI Listing:
https://doi.org/10.1109/IA356718.2022.00010

Abstract

Decomposing sparse matrices into lower and upper triangular matrices (sparse LU factorization) is a key operation in many computational scientific applications. We developed SparseLU, a sparse linear algebra library that implements a new algorithm for LU factorization on general sparse matrices. The new algorithm divides the input matrix into tiles to which OpenMP tasks are created for factorization computation, where only tiles that contain nonzero elements are computed. For comparative performance analysis, we used the reference library SuperLU. Testing was performed on synthetically generated matrices which replicate the conditions of the real-world matrices. SparseLU is able to reach a mean speedup of ~29× compared to SuperLU.

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