NDLI: Efficient sparse LU factorization with partial pivoting on distributed memory architectures

Content Provider	IEEE Xplore Digital Library
Author	Cong Fu Xiangmin Jiao Tao Yang
Copyright Year	1990
Abstract	A sparse LU factorization based on Gaussian elimination with partial pivoting (GEPP) is important to many scientific applications, but it is still an open problem to develop a high performance GEPP code on distributed memory machines. The main difficulty is that partial pivoting operations dynamically change computation and nonzero fill-in structures during the elimination process. This paper presents an approach called S* for parallelizing this problem on distributed memory machines. The S* approach adopts static symbolic factorization to avoid run-time control overhead, incorporates 2D L/U supemode partitioning and amalgamation strategies to improve caching performance, and exploits irregular task parallelism embedded in sparse LU using asynchronous computation scheduling. The paper discusses and compares the algorithms using 1D and 2D data mapping schemes, and presents experimental studies on Cray-T3D and T3E. The performance results for a set of nonsymmetric benchmark matrices are very encouraging, and S* has achieved up to 6.878 GFLOPS on 128 T3E nodes. To the best of our knowledge, this is the highest performance ever achieved for this challenging problem and the previous record was 2.583 GFLOPS on shared memory machines.
Sponsorship	IEEE Computer Society
Starting Page	109
Ending Page	125
Page Count	17
File Size	390043
File Format	PDF
ISSN	10459219
Volume Number	9
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Memory architecture Sparse matrices Parallel processing Concurrent computing Symmetric matrices Numerical stability Processor scheduling Equations Data structures Runtime
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computational Theory and Mathematics Hardware and Architecture

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