Specialising Guava’s Cache to Reduce Energy Consumption

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 .…”

Search-Based Energy Optimization of Some Ubiquitous Algorithms

“…This means that it can be parallelised, or executed simultaneously with other programs. Previous work has successfully used OPACITOR to optimize and reduce the energy consumption of Google Guava's 3 IMMUTABLEMULTIMAP class [23], as well as OpenTripPlanner 4 [38]. In each of these cases, a comparison was made with an alternative timing-based estimation tool JALEN [15] in order to corroborate the assertion that the technique used by OPACITOR is effective and generates reliable measurements.…”

“…The bytecode level estimates of energy use were taken from Hao et al [18], where hardware measurements found the model to be within 10% of the ground truth for a set of mobile applications from the Google Play store. In our previous work [23], [38], we also validated the model by comparing against run-time based models of energy use. Additionally, in practice energy use is likely to follow a conditional distribution rather than remaining constant per-bytecode.…”

Approximate Oracles and Synergy in Software Energy Search Spaces

“…D.addRecord(δ, t, J, a, p ) 15: end for 16: end for 17: end for 18: return D modified program correspond to the output of the original program (modulo the testcases). However, the results of the approximation value can be non-zero, with higher approximation values corresponding to greater degrees of approximation.…”

“…It would be possible to expose optimisations found in sourcecode to a level in which they may be tuned as if they were 'traditional' parameters. This is part of a growing area within genetic improvement known as 'deep parameter tuning', which has been used to optimise the execution time and memory consumption of C standard library's malloc function [62], optimise a face-detection algorithm's execution time while permitting a degradation in its accuracy [13], [14], and reduce the energy consumption of Google Guava's CacheBuilder class [17].…”