Post-compiler software optimization for reducing energy

Abstract

Modern compilers typically optimize for executable size and speed, rarely exploring non-functional properties such as power efficiency. These properties are often hardwarespecific, 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.

Visualising the Search Landscape of the Triangle Program

“…by removing bugs [8,9,10,11,12,13,14,15,16] or adding to its abilities [17,18,19,20,21,22]. Non-functional improvements that have been considered or results reported include: faster code [23,24], code which uses less energy [25,26,27,28,29,30,31,32,33,34] or less memory [35], and automatic parallelisation [36,37,38] and automatic porting [39] and embedded systems [40,41,25,42,43,44,45] as well as refactorisation [46], reverse engineering [47,48] and software product lines [49,50]. There is very much a GI flavour in the air with a three-fold increase in GI publications (as measured by GI papers in the genetic programming bibliography) since the first GI workshop [51] was first mooted (October, 7 2014) 1 .…”

Automated Transplantation of Call Graph and Layout Features into Kate

“…Our work is closely related to recent achievements in genetic improvement, which have been able to dramatically speed up real world systems [9,14,15], port between languages [8], balance memory consumption and execution time [16], reduced energy consumption [3,13] and fix bugs [10]. Most closely related to our approach is work on auto-specialisation using transplantation [11] and grow and graft genetic improvement [6].…”

API-Constrained Genetic Improvement

“…It usually does this by searching for a set of edits that are performed on the software system to be improved, such that the desired functional behaviour of the original is retained, while some functional [10,5] and/or non-functional [15,11] aspects are improved. There has been a recent upsurge of activity in this area, with results demonstrating that genetic improvement is able to improve many different properties of systems, including dynamic memory use [20], speed of execution [9,17] and energy consumption [1,14], as well as augmenting and fixing broken functionality [10,5].…”