Parallel supercomputing: Advanced methods, algorithms and software for large-scale problems. Final report, August 1, 1987--July 31, 1994
The focus of the subject DOE sponsored research concerns parallel methods, algorithms, and software for complex applications such as those in coupled fluid flow and heat transfer. The research has been directed principally toward the solution of large-scale PDE problems using iterative solvers for finite differences and finite elements on advanced computer architectures. This work embraces parallel domain decomposition, element-by-element, spectral, and multilevel schemes with adaptive parameter determination, rational iteration and related issues. In addition to the fundamental questions related to developing new methods and mapping these to parallel computers, there are important software issues. The group has played a significant role in the development of software both for iterative solvers and also for finite element codes. The research in computational fluid dynamics (CFD) led to sustained multi-Gigaflop performance rates for parallel-vector computations of realistic large scale applications (not computational kernels alone). The main application areas for these performance studies have been two-dimensional problems in CFD. Over the course of this DOE sponsored research significant progress has been made. A report of the progression of the research is given and at the end of the report is a list of related publications and presentations over the entire grant period.
- Research Organization:
- Univ. of Texas, Austin, TX (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research, Washington, DC (United States)
- DOE Contract Number:
- FG05-87ER25048
- OSTI ID:
- 674846
- Report Number(s):
- DOE/ER/25048--T9; ON: DE99000726
- Country of Publication:
- United States
- Language:
- English
Similar Records
A research program in finite element analysis and iterative solution on advanced processors
Parallel supercomputing: Advanced methods, algorithms and software for large-scale problems