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Monte Carlo techniques in code optimization

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Published:05 October 1982Publication History
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Abstract

Effective optimization of FPS Array Processor assembly language (APAL) is difficult. Instructions must be rearranged and consolidated to minimize periods during which the functional units remain idle or perform unnecessary tasks. Register conflicts and branches cause complications. Deterministic algorithms to arrange instructions traditionally use complex heuristics which are tailored to specific inputs. A non-deterministic approach can be simpler and effective on a large class of inputs. This is a progress report on the “Monte Carlo” optimizer under construction at Cornell University by the authors. This optimizer randomly modifies the text of an APAL program without changing its meaning. Modifications which improve the program are favored. A set of six elementary transformations are the basis for modifications.

References

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  8. 8 N. Gimbrone, K. Wilson "Monte Carlo Optimization" in ARRAY proceedings 1981Google ScholarGoogle Scholar

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  1. Monte Carlo techniques in code optimization

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            cover image ACM SIGMICRO Newsletter
            ACM SIGMICRO Newsletter  Volume 13, Issue 4
            Dec. 1982
            169 pages
            ISSN:1050-916X
            DOI:10.1145/1014194
            Issue’s Table of Contents

            Copyright © 1982 Authors

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 5 October 1982

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