Post-compiler software optimization for reducing energy

  • Authors:

  • Eric Schulte;Jonathan Dorn;Stephen Harding;Stephanie Forrest;Westley Weimer

  • Affiliations:

  • University of New Mexico, Albuquerque, NM, USA;University of Virginia, Charlottesville, VA, USA;University of New Mexico, Albuquerque, NM, USA;University of New Mexico, Albuquerque, NM, USA;University of Virginia, Charlottesville, VA, USA

  • Venue:
  • Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
  • Year:
  • 2014

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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 optimize, and may not be amenable to standard dataflow optimizations. We present a general post-compilation approach called Genetic Optimization 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, superoptimization, evolutionary computation and mutational robustness. GOA searches for program variants that retain required functional behavior while improving non-functional behavior, using characteristic workloads and predictive modeling to guide the search. The resulting optimizations 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 20%, both for a large AMD system and a small Intel system, while maintaining program functionality on target workloads.