HYLU: Hybrid Parallel Sparse LU Factorization
This work provides a faster solver for sparse linear systems in computational applications, but it is incremental as it builds on existing LU factorization methods with hybrid kernels.
The paper tackles the problem of efficiently solving sparse linear systems on multi-core shared-memory architectures by introducing HYLU, a hybrid parallel LU factorization solver, which outperforms Intel MKL PARDISO by geometric means of 2.36X for one-time solving and 2.90X for repeated solving on 37 sparse matrices.
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.