ON THE DESIGN AND MODELING OF SPECIAL PURPOSE PARALLEL PROCESSING SYSTEMS (COMPUTER ARCHITECTURE)
Abstract
As the capabilities of computing machinery grow, so does the diverse variety of their applications. The feasibility of many approaches to these applications depends solely upon the existence of computing machinery capable of performing these tasks within a given time constraint. Because the majority of the available computing machinery is general purpose in nature, tasks that do not require general purpose facilities, but that do require high throughput, are condemned to execution on expensive general purpose hardware. This research describes several tasks that require fast computing machinery. These tasks do not require general purpose facilities in the sense that the computing machinery used will only perform a fixed set of tasks. Some of the tasks are simple in nature, but are required to execute on very large data sets. Other tasks are computationally intensive in addition to possibly involving large data sets. Both simple and complex algorithms are considered. The discussion includes a description of the tasks. All of the above tasks are useful; however, their value is determined in part by the time required to perform them. This work discusses three architectures for performing remote sensing tasks. These architectures can execute the described tasks more quickly than conventionally available hardware. The discussion extends to the realm of designing macro-pipelined distributed computer systems for special purpose applications. Nine parameters are introduced along with a proposal for an algorithmic approach to designing a computer system for a special application. The parameters are then applied to an isolated word recognition system. For may tasks (especially those involving feedback), it is undesirable to use synchronous parallelism. A study, including a probabilistic model, of the effects of using asynchronous stages in the macro-pipeline is presented. Simulation is used to verify the results.
Degree
Ph.D.
Subject Area
Computer science
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