Title :
Memory-adaptive parallel sparse Cholesky factorization
Author :
Eswar, Kalluri ; Huang, Chua-Huang ; Sadayappan, P.
Author_Institution :
Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA
Abstract :
The problem of Cholesky factorization of sparse positive-definite matrices on distributed-memory multiprocessors is considered. A column-based algorithm with the ability to adapt to the amount of memory available on each processor is presented. Exploiting the available memory allows the local computation on each processor to be ordered so that good local efficiencies and dynamic load balance are achieved. A proof that this distributed algorithm is deadlock-free is given. Experimental results of an implementation of this algorithm on an Intel iPSC/860 multiprocessor system are reported
Keywords :
concurrency control; distributed memory systems; mathematics computing; matrix algebra; parallel algorithms; resource allocation; Cholesky factorization; Intel iPSC/860 multiprocessor system; column-based algorithm; deadlock-free algorithm; distributed algorithm; distributed-memory multiprocessors; dynamic load balance; local efficiencies; memory-adaptive parallel algorithm; ordered local computation; sparse positive-definite matrices; Aggregates; Data structures; Distributed algorithms; Distributed computing; Information science; Multiprocessing systems; Partitioning algorithms; Sparse matrices; System recovery;
Conference_Titel :
Scalable High-Performance Computing Conference, 1994., Proceedings of the
Conference_Location :
Knoxville, TN
Print_ISBN :
0-8186-5680-8
DOI :
10.1109/SHPCC.1994.296660
