Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava

Abstract

Abstract. In this work we use metaheuristic search to improve Google's Guava library, finding a semantically equivalent version of com.google. common.collect.ImmutableMultimap with reduced energy consumption. Semantics-preserving transformations are found in the source code, using the principle of subtype polymorphism. We introduce a new tool, Opacitor, to deterministically measure the energy consumption, and find that a statistically significant reduction to Guava's energy consumption is possible. We corroborate these results using Jalen, and evaluate the performance of the metaheuristic search compared to an exhaustive search-finding that the same result is achieved while requiring almost 200 times fewer fitness evaluations. Finally, we compare the metaheuristic search to an independent exhaustive search at each variation point, finding that the metaheuristic has superior performance.

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 .…”

Exploring Fitness and Edit Distance of Mutated Python Programs

“…Langdon's GI implementation has furthermore been used by others for specializing and optimizing the execution time of MiniSAT [30], a boolean satisfiability solver and for optimizing power consumption of that same solver [5,6]. Many others have applied or suggested GI for improving non-functional properties such as execution time [9,10,35,42], energy consumption [7,8,13,40,43] and memory usage [32,44].…”

Deep Parameter Tuning of Concurrent Divide and Conquer Algorithms in Akka

“…Kocsis et al also automatically repaired 451 systematic errors in the implementation of the Apache Hadoop HPC framework [17], whilst simultaneously significantly improving performance. In addition to the work improving Quicksort for energy efficiency mentioned in Section 2, Burles et al [6], also obtained a 24% improvement in energy consumption by optimising a single widely-used class, ImmutableMultimap, in Google's Guava collection library. They used a Genetic Algorithm and constrained the search space via the behavioural contracts of Object-Orientation.…”