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.

翻译：本文介绍HYLU，一种基于混合并行LU分解的通用求解器，旨在高效求解多核共享内存架构上的稀疏线性系统（Ax=b）。HYLU的关键技术特点是集成混合数值内核，使其能够适应系数矩阵的各种稀疏模式。对来自SuiteSparse Matrix Collection的37个稀疏矩阵的测试表明，在数值分解阶段，HYLU在几何平均上比Intel MKL PARDISO性能提升2.36倍（单次求解）和2.90倍（重复求解）。HYLU可从https://github.com/chenxm1986/hylu下载。