Post-compiler Software Optimization for Reducing Energy

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 Al-gorithm (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 vari-ants that retain required functional behavior while improving non-functional behavior, using characteristic workloads and predictive modeling to guide the search. The resulting op-timizations are validated using physical performance mea-surements and a larger held-out test suite. Our experimental results on PARSEC benchmark programs show average en-ergy reductions of 20%, both for a large AMD system and a small Intel system, while maintaining program functionality on target workloads