logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Data-parallel sparse LU factorization

delete2006-07-25
delete4
PRE
AI
J
John M. Conroy
S
Steven G. Kratzer
R
Robert F. Lucas
A
Aaron E. Naiman
DOI：10.1137/S1064827594276412delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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.
Keywords:
parallel algorithms
sparse linear systems
direct method
data-parallel algorithms
SIMD

Journal

SIAM Journal on Scientific Computing cover
SIAM Journal on Scientific Computing
IF:
2.6
Papers:
5.1K
Citations:
1.8W

Organization

No organization information available