Design and analysis of parallel adaptive algorithms for composite decision processes
This dissertation presents new approaches to the design and analysis of parallel adaptive algorithms for multiple instruction stream multipole data stream (MIMD) machines. A composite decision process (cdp) is a model for many problems from the field of artificial intelligence. The mathematical structure modeled by a cdp includes both the algebraic structure of the domain set of the problem and the functional structure of the problem. A dynamic algorithm is a parallel algorithm with its control structure consisting of (1) an adaptive task structure, and (2) eager computation enabling mechanism. The eager computation is an enabling mechanism of parallel computations governed by processor availabilities. In the algorithm analysis, focus is on the utility of computing power in the designed algorithm and the utility of the accumulated information in reducing the cost of search effort. The relation of these two aspects to the speed-up ratio is investigated. The analysis is called a dynamic analysis because it focuses on these major dynamic features of the parallel processes. The quantitative analysis confirms that, in parallel adaptive search, to increase parallelism one must accept dynamic task assignment, and must have dynamic modification of global tasks in order to make best use of accumulated information.
- Research Organization:
- Maryland Univ., College Park (USA)
- OSTI ID:
- 6683948
- Resource Relation:
- Other Information: Thesis (Ph. D.)
- Country of Publication:
- United States
- Language:
- English
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