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788 p. ; 27 cm

788 p. ; 27 cm

788 p. ; 27 cm

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 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 major thrust of our effort was focused on the theory and practice of responsive (fault-tolerant, real-time) computing in parallel and distributed processing environments. New efficient methods of system testing have been developed which shorten a multiprocessor testing time by orders of magnitude and, therefore, can be used at system booting (previous techniques were prohibitively long. A new design framework for responsive computing was designed and is being implemented for validation. This framework for responsive computing was designed and is being implemented for validation. This framework is based on consensus which can be used to provide synchronization, reliable communication, fault diagnosis, checkpointing and even scheduling in multiprocessor environments. We have formalized and quantified the space-time tradeoff for efficient fault recovery. The system model is a graph, and we were especially successful in analysis of meshes and hypercubes. We developed a new method called naturally redundant algorithms which allows efficient implementation of application-specific techniques

The demand for very high speed data processing coupled with falling hardware costs has made large-scale parallel and distributed computer systems both desirable and feasible. Two modes of parallel processing are single instruction stream-multiple data stream (SIMD) and multiple instruction stream - multiple data stream (MIMD). PASM, a partitionable SIMD/MIMD system, is a reconfigurable multimicroprocessor system being designed for image processing and pattern recognition. An important component of these systems is the interconnection network, the mechanism for communication among the computation nodes and memories. Assuring high reliability for such complex systems is a significant task. Thus, a crucial practical aspect of an interconnection network is fault tolerance. In answer to this need, the Extra Stage Cube (ESC), a fault-tolerant, multistage cube-type interconnection network, is defined. The fault tolerance of the ESC is explored for both single and multiple faults, routing tags are defined, and consideration is given to permuting data and partitioning the ESC in the presence of faults. The ESC is compared with other fault-tolerant multistage networks. Finally, reliability of the ESC and an enhanced version of it are investigated. Keywords: Theses.