Data-Parallel Sparse Factorization

Abstract

Sparse matrix factorization is a computational bottleneck in many scientific and engineering problems. This paper examines the problem of factoring large sparse matrices on data-parallel computers. A multifrontal approach is presented in which only the fine-grain concurrency found within the elimination of each supernode is exploited. Throughput approaching that of large dense matrix factorizations is demonstrated on two data-parallel systems, the MasPar MP-2 and the Thinking Machines CM-5.