By Topic

On mapping data and computation for parallel sparse Cholesky factorization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Eswar, Kalluri ; Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA ; Huang, Chua-Huang ; Sadayappan, P.

When performing the Cholesky factorization of a sparse matrix on a distributed-memory multiprocessor, the methods used for mapping the elements of the matrix and the operations constituting the factorization to the processors can have a significant impact on the communication overhead incurred. This paper explores how two techniques, one used when mapping dense Cholesky factorization and the other used when mapping sparse Cholesky factorization, can be integrated to achieve a communication-efficient parallel sparse Cholesky factorization. Two localizing techniques to further reduce the communication overhead are also described. The mapping strategies proposed here, as well as other previously proposed strategies fit into the unifying framework developed in this paper. Communication statistics for sample sparse matrices are included

Published in:

Frontiers of Massively Parallel Computation, 1995. Proceedings. Frontiers '95., Fifth Symposium on the

Date of Conference:

6-9 Feb 1995