Solving planar systems of equations on distributed-memory multiprocessors
The advent of VLSI has made extremely powerful, cost-effective parallel computing systems practical. This has been accompanied by a tremendous increase in the demand for computing power as ever-more complicated phenomena are being studied by numerical techniques. Unfortunately, the software developed over the last thirty years for solving these problems has been geared toward sequential or vector machines and is not directly applicable to the emerging highly concurrent computers. To address this problem, both direct and iterative sparse solvers have been developed for large-scale, message-passing multiprocessors. A new distributed multifrontal (DMF) algorithm for sparse Gaussian elimination on parallel computers is presented. This method uses the nested dissection reordering heuristic to extract separators from the graph of the matrix, thereby partitioning the matrix into disjoint blocks that can be allocated to the processors. Symmetric positive definite systems can often be solved faster using iterative techniques. Therefore, a parallel implementation of one of the most successful iterative methods, the Incomplete Cholesky Preconditioned Conjugate Gradient (ICCG) algorithm, was implemented.
- Research Organization:
- Stanford Univ., CA (USA)
- OSTI ID:
- 6210742
- Resource Relation:
- Other Information: Thesis (Ph. D.)
- Country of Publication:
- United States
- Language:
- English
Similar Records
Domain decomposition techniques for solving elliptic partial differential equations on multiprocessors
Experience with the incomplete Cholesky conjugate gradient method in a diffusion code
Related Subjects
ARRAY PROCESSORS
DISTRIBUTED DATA PROCESSING
PARALLEL PROCESSING
GAUSSIAN PROCESSES
IMPLEMENTATION
ITERATIVE METHODS
MEMORY MANAGEMENT
DATA PROCESSING
PROCESSING
PROGRAMMING
990210* - Supercomputers- (1987-1989)
990300 - Information Handling