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Parallel Sparse LU Factorization With Machine-Learning Method on Multi-core Processors

Since the emergence of multi-core systems, many efforts must be taken to make existing software take advantage of these new architectures. We exploit dense matrix kernels and node parallelism in the sparse LU factorization, at the same time, also relying on third-party optimized multithreaded BLAS libraries. We introduce multi-threaded unified management mode and Task parallel optimization, targeting multi-core architectures. Our approach avoids a deep redesign and fully benefits from the numerical kernels and features of the original code. In this context, we propose simple approaches to take advantage of NUMA architectures. The performance gains are analyzed in detail on test problems, compared with MKL libraries.