NDLI: Sparse LU Factorization with Partial Pivoting on Distributed Memory Machines

Content Provider	IEEE Xplore Digital Library
Author	Cong Fu Tao Yang
Copyright Year	1996
Description	Author affiliation: University of California, Santa Barbara (Cong Fu)
Abstract	Sparse LU factorization with partial pivoting is important to many scientific applications, but the effective parallelization of this algorithm is still an open problem. The main difficulty is that partial pivoting operations make structures of L and U factors unpredictable beforehand. This paper presents a novel approach called S* for parallelizing this problem on distributed memory machines. S* incorporates static symbolic factorization to avoid run-time control overhead and uses nonsymmetric L/U supernode partitioning and amalgamation strategies to maximize the use of BLAS-3 routines. The irregular task parallelism embedded in sparse LU is exploited using the RAPID run-time system which optimizes asynchronous communication and task scheduling. The experimental results on the Cray0-T3D with a set of Harwell-Boeing nonsymmetric matrices are very encouraging and good scalability has been achieved. Even compared to a highly optimized sequential code, the parallel speedups are still impressive considering the current status of sparse LU research.
Starting Page	31
Ending Page	31
File Size	1409407
Page Count	1
File Format	PDF
ISBN	0897918541
DOI	10.1109/SUPERC.1996.183533
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-01-01
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Runtime Communication system control Asynchronous communication Sparse matrices Scalability Symbolic factorization Sparse LU factorization Column partial pivoting Dense structures Task scheduling Irregular parallelism Run-time support
Content Type	Text
Resource Type	Article

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