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Multi-objective evolutionary auto-tuning for optimising algorithm speed and cache memory usage

Published: 06 July 2013 Publication History

Abstract

Modern CPUs are complex with hierarchical cache memory levels, vector instruction sets, instruction level parallelism and multiple processor cores. Hence, extracting the maximum performance for a given algorithm is a complex task and can require the optimisation of a number of parameters. This paper will demonstrate the use of an evolutionary approach to tune a matrix multiplication algorithm in terms of both execution speed and also cache memory usage. Moreover, it will be shown that these objectives conflict to some degree. Hence, a multi-objective evolutionary tuning approach is demonstrated that optimises for both of these objectives establishing a Pareto front of solutions.

References

[1]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. Trans. Evol. Comp, 6(2):182--197, Apr. 2002.
[2]
J. Dongarra and A. R. Hinds. Unrolling loops in FORTRAN. Softw., Pract. Exper., 9(3):219--226, 1979.
[3]
G. Golub and C. Loan. Matrix computations. Johns Hopkins series in the mathematical sciences. Johns Hopkins University Press, 1989.

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  1. Multi-objective evolutionary auto-tuning for optimising algorithm speed and cache memory usage

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    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. auto-tuning
    2. multi-objective genetic algorithms

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    GECCO '13
    Sponsor:
    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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