Design and Implementation of Parallelized Cholesky Factorization

The bottleneck of most data analyzing systems, signal processing systems, and intensive computing systems is matrix decomposition. The Cholesky factorization of a sparse matrix is an important operation in numerical algorithms field. This paper presents a Multi-phased Parallel Cholesky Factorization...

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Published inHigh Performance Computing and Applications pp. 390 - 397
Main Authors Wang, Bailing, Ge, Ning, Peng, Hongbo, Wei, Qiong, Li, Guanglei, Gong, Zhigang
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Cholesky Factorization
Computation Kernel
Data Block
Diagonal Block
Symmetric Positive Definite Matrix
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Summary:The bottleneck of most data analyzing systems, signal processing systems, and intensive computing systems is matrix decomposition. The Cholesky factorization of a sparse matrix is an important operation in numerical algorithms field. This paper presents a Multi-phased Parallel Cholesky Factorization (MPCF) algorithm, and then gives the implementation on a multi-core machine. A performance result shows that the system can reach 85.7 Gflop/s on a single PowerXCell processor and bulk of computation can reach to 94% of peak performance.
ISBN:9783642118418
3642118410
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-11842-5_54