Load distribution schemes are presented which minimize the total data traffic in the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems with local and shared memory. The total data traffic in factoring an n x n sparse, symmetric, positive definite matrix representing an n-vertex regular 2-D grid graph using n (sup alpha), alpha is equal to or less than 1, processors are shown to be O(n(sup 1 + alpha/2)). It is O(n(sup 3/2)), when n (sup alpha), alpha is equal to or greater than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal. The schemes allow efficient use of up to O(n) processors before the total data traffic reaches the maximum value of O(n(sup 3/2)). The partitioning employed within the scheme, allows a better utilization of the data accessed from shared memory than those of previously published methods.
[1]
Vijay K. Naik,et al.
Communication Requirements of Sparse Cholesky Factorization with Nested Dissection Ordering
,
1987,
PPSC.
[2]
A. George.
Nested Dissection of a Regular Finite Element Mesh
,
1973
.
[3]
J. Pasciak,et al.
Computer solution of large sparse positive definite systems
,
1982
.
[4]
Alan George,et al.
Communication results for parallel sparse Cholesky factorization on a hypercube
,
1989,
Parallel Comput..
[5]
Michael T. Heath,et al.
Sparse Cholesky factorization on a local-memory multiprocessor
,
1988
.
[6]
D. Rose,et al.
Generalized nested dissection
,
1977
.