Post-compiler Software Optimization for Reducing Energy

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{schulte2014optimization,

• author = "Eric Schulte and Jonathan Dorn and Stephen Harding and Stephanie Forrest and Westley Weimer",
• title = "Post-compiler Software Optimization for Reducing Energy",
• booktitle = "Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS'14",
• year = "2014",
• pages = "639--652",
• address = "Salt Lake City, Utah, USA",
• publisher_address = "New York, NY, USA",
• month = "1-5 " # mar,
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming, genetic improvement, GOA, Evolutionary Computation, Power Modelling, Assembly Code, Compilation, Profile-guided Optimisation, Superoptimization, Mutational Robustness",
• isbn13 = "978-1-4503-2305-5",
• URL = "https://squareslab.github.io/papers-repo/pdfs/schulte-asplos2014.pdf",
• URL = "http://doi.acm.org/10.1145/2654822.2541980",
• DOI = "doi:10.1145/2541940.2541980",
• acmid = "2541980",
• size = "13 pages",
• abstract = "Modern compilers typically optimize for executable size and speed, rarely exploring non-functional properties such as power efficiency. These properties are often hardware-specific, time-intensive to optimise, and may not be amenable to standard dataflow optimisations. We present a general post-compilation approach called Genetic Optimisation Algorithm (GOA), which targets measurable non-functional aspects of software execution in programs that compile to x86 assembly. GOA combines insights from profile-guided optimization, superoptimisation, evolutionary computation and mutational robustness. GOA searches for program variants that retain required functional behaviour while improving non-functional behavior, using characteristic workloads and predictive modeling to guide the search. The resulting optimisations are validated using physical performance measurements and a larger held-out test suite. Our experimental results on PARSEC benchmark programs show average energy reductions of 20percent, both for a large AMD system and a small Intel system, while maintaining program functionality on target workloads.",
• notes = "

  Steady state population 512. Linux gcc, Perf. blackscholes, bodytrack, ferret, fluidanimate, freqmine, swaptions, vips, x264 (MPEG). https://github.com/eschulte/goa",

}

Genetic Programming entries for Eric Schulte Jonathan Dorn Stephen Harding Stephanie Forrest Westley Weimer

Citations