An Improved Algorithm for Parallel Sparse LU Decomposition on a Distributed-Memory Multiprocessor

Abstract

In this paper we present a new parallel algorithm for the LU decomposition of a general sparse matrix Among its features are matrix redistribution at regular intervals and a dynamic pivot search strategy that adapts itself to the number of pivots produced. Experimental results obtained on a network of transputers show that these features considerably improve the performance.