By Topic

A high performance sparse Cholesky factorization algorithm for scalable parallel computers

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
$33 $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

2 Author(s)
G. Karypis ; Dept. of Comput. Sci., Minnesota Univ., Minneapolis, MN, USA ; V. Kumar

This paper presents a new parallel algorithm for sparse matrix factorization. This algorithm uses subforest-to-subcube mapping instead of the subtree-to-subcube mapping of another recently introduced scheme by A. Gupta and V. Kumar (1994). Asymptotically, both formulations are equally scalable on a wide range of architectures and a wide variety of problems. But the subtree-to-subcube mapping of the earlier formulation causes significant load imbalance among processors, limiting overall efficiency and speedup. The new mapping largely eliminates the load imbalance among processors. Furthermore, the algorithm has a number of enhancements to improve the overall performance substantially. This new algorithm achieves up to 20GFlops on a 1024-processor Cray T3D for moderately large problems. To our knowledge, this is the highest performance ever obtained on an MPP for sparse Cholesky factorization

Published in:

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

Date of Conference:

6-9 Feb 1995