A partitioning strategy for parallel sparse Cholesky factorization
This paper presents a solution to the problem of partitioning the work for sparse matrix factorization on a multiprocessor system. The goal of this partitioning strategy is to achieve load balancing and a high degree of concurrency among the processors while reducing the amount of processor-to-processor data communication. The task assignment strategy is based on the structure of the elimination tree for a given ordering, and can be applied to arbitrarily unbalanced trees. This is important because popular fill-reducing ordering methods, such as the minimum degree algorithm, often produce unbalanced elimination trees. Results from the Intel iPSC/2 are presented for various finite-element problems using both nested dissection and minimum degree orderings 14 refs., 12 figs., 6 tabs.
- Research Organization:
- Oak Ridge National Lab., TN (USA)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 6547279
- Report Number(s):
- ORNL/TM-10937; ON: DE89005690
- Country of Publication:
- United States
- Language:
- English
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