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首页> 美国政府科技报告 >Distributed Computing for Signal Processing: Modeling of Asynchronous Parallel Computation. Appendix D. Analysis of MIMD (Multiple Instruction Streams, Multiple Data Streams) Algorithms: Features, Measurements, and Results
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Distributed Computing for Signal Processing: Modeling of Asynchronous Parallel Computation. Appendix D. Analysis of MIMD (Multiple Instruction Streams, Multiple Data Streams) Algorithms: Features, Measurements, and Results

机译：信号处理的分布式计算：异步并行计算的建模。附录D. mImD（多指令流，多数据流）算法的分析：特征，测量和结果

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Analysis of parallel algorithms for MIMD (Multiple Instruction streams, Multiple data streams) machines is often difficult. Much work in the past has focused on SISD (Single Instruction and Data Streams (conventional)) and SIMD(Single Instruction stream, Multiple Instruction system (vector)) algorithms. Most of this work applies in MIMD systems, yet there are several significant problems that arise. This thesis focuses on these problems and proposes solutions to them. An image processing problem is analyzed for parallelism. Measures of parallelism are proposed. With these measures in mind, the image processing problem is again analyzed and several common parallel languages are surveyed. With this background, a set of language and machine independent MIMD constructs is proposed, and it is shown how these can be used on several forms of traditional analysis. (Thesis)

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