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Genetic improvement in code interpreters and compilers

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SPLASH Companion 2017: Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity

Pages 7 - 9

https://doi.org/10.1145/3135932.3135934

Published: 22 October 2017 Publication History

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Abstract

Modern compilers provide code optimizations before and during run-time, thus moving required domain knowledge about the compilation process away from the developer and speeding up resulting software. These optimizations are often based on formal proof, or alternatively have recovery paths as backup. Genetic improvement (GI), a field of science utilizing genetic programming, a stochastic optimization technique, has been previously utilized to fix bugs in software and improve non-functional software requirements.

This work proposes to research the applicability of GI in an offline phase directly at the interpreter or compiler level. The primary goal is to reformulate existing source code in such a way that existing optimizations can be applied in order to increase performance even further and requiring even less domain knowledge from the developer about a programming language and/or compiler. From these reformulations, patterns can be identified that allow code restructuring without the overhead GI poses.

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Cited By

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Dorfmeister DKrauss OCoello Coello C(2020)Integrating heuristiclab with compilers and interpreters for non-functional code optimizationProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3398103(1580-1588)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3398103
Krauss OTilevich EMössenböck H(2018)Towards a framework for stochastic performance optimizations in compilers and interpretersProceedings of the 15th International Conference on Managed Languages & Runtimes10.1145/3237009.3237024(1-7)Online publication date: 12-Sep-2018
https://dl.acm.org/doi/10.1145/3237009.3237024
Krauss OMössenböck HAffenzeller MTakadama K(2018)Dynamic fitness functions for genetic improvement in compilers and interpretersProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208308(1590-1597)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208308

Index Terms

Genetic improvement in code interpreters and compilers
1. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering
    2. Software post-development issues
      1. Software evolution

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Published In

SPLASH Companion 2017: Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity

October 2017

56 pages

ISBN:9781450355148

DOI:10.1145/3135932

General Chair:
Gail C. Murphy
University of British Columbia, Canada

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAda: ACM Special Interest Group on Ada Programming Language

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2017

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SPLASH '17

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SIGPLAN

SPLASH '17: Conference on Systems, Programming, Languages, and Applications: Software for Humanity

October 22 - 27, 2017

BC, Vancouver, Canada

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Cited By

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Dorfmeister DKrauss OCoello Coello C(2020)Integrating heuristiclab with compilers and interpreters for non-functional code optimizationProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3398103(1580-1588)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3398103
Krauss OTilevich EMössenböck H(2018)Towards a framework for stochastic performance optimizations in compilers and interpretersProceedings of the 15th International Conference on Managed Languages & Runtimes10.1145/3237009.3237024(1-7)Online publication date: 12-Sep-2018
https://dl.acm.org/doi/10.1145/3237009.3237024
Krauss OMössenböck HAffenzeller MTakadama K(2018)Dynamic fitness functions for genetic improvement in compilers and interpretersProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208308(1590-1597)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208308

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Genetic improvement in the shackleton framework for optimizing LLVM pass sequences

Automated design of algorithms and genetic improvement: contrast and commonalities