Techniques for improving the performance of sparse matrix factorization on multiprocessor workstations

The problem of factoring large sparse systems of equations on high-performance multiprocessor workstations is investigated. A parallel factorization code is described which utilizes the supernodal structure of the matrix to substantially reduce the number of memory references. The authors also propose enhancements that significantly reduce the overall cache miss rate, resulting in greatly increased factorization performance. Experimental results from executions on the Silicon Graphics 4D/380 multiprocessor are presented. Using eight processors, the parallel supernodal code achieves a computation rate of approximately 40 MFLOPS when factoring a range of benchmark matrices. This is more than twice as fast as previously used parallel nodal approaches