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Multiplexed Pipelining : A Cost Effective Loop Transformation Technique, Satish Pai

Dissertations and Theses

Parallel processing has gained increasing importance over the last few years. A key aim of parallel processing is to improve the execution times of scientific programs by mapping them to many processors. Loops form an important part of most computational programs and must be processed efficiently to get superior performance in terms of execution times. Important examples of such programs include graphics algorithms, matrix operations (which are used in signal processing and image processing applications), particle simulation, and other scientific applications. Pipelining uses overlapped parallelism to efficiently reduce execution time.