HYLU: Hybrid Parallel Sparse LU Factorization

Fuente: arXiv
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Main Author: Chen, Xiaoming
Format: Preprint
Published: 2025
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author Chen, Xiaoming
author_facet Chen, Xiaoming
contents This article introduces HYLU, a hybrid parallel LU factorization-based general-purpose solver designed for efficiently solving sparse linear systems (Ax=b) on multi-core shared-memory architectures. The key technical feature of HYLU is the integration of hybrid numerical kernels so that it can adapt to various sparsity patterns of coefficient matrices. Tests on 37 sparse matrices from SuiteSparse Matrix Collection reveal that HYLU outperforms Intel MKL PARDISO in the numerical factorization phase by geometric means of 2.36X (for one-time solving) and 2.90X (for repeated solving). HYLU can be downloaded from https://github.com/chenxm1986/hylu.
format Preprint
id arxiv_https___arxiv_org_abs_2509_07690
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle HYLU: Hybrid Parallel Sparse LU Factorization
Chen, Xiaoming
Hardware Architecture
Distributed, Parallel, and Cluster Computing
Mathematical Software
Numerical Analysis
G.1.3; G.4
This article introduces HYLU, a hybrid parallel LU factorization-based general-purpose solver designed for efficiently solving sparse linear systems (Ax=b) on multi-core shared-memory architectures. The key technical feature of HYLU is the integration of hybrid numerical kernels so that it can adapt to various sparsity patterns of coefficient matrices. Tests on 37 sparse matrices from SuiteSparse Matrix Collection reveal that HYLU outperforms Intel MKL PARDISO in the numerical factorization phase by geometric means of 2.36X (for one-time solving) and 2.90X (for repeated solving). HYLU can be downloaded from https://github.com/chenxm1986/hylu.
title HYLU: Hybrid Parallel Sparse LU Factorization
topic Hardware Architecture
Distributed, Parallel, and Cluster Computing
Mathematical Software
Numerical Analysis
G.1.3; G.4
url https://arxiv.org/abs/2509.07690