Basios, M;
Li, L;
Wu, F;
Kanthan, L;
Barr, E;
(2018)
Darwinian Data Structure Selection.
In: Leavens, Gary T., (ed.)
Proceedings of ESEC/FSE '18 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering.
ACM: FL, USA.
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Abstract
Data structure selection and tuning is laborious but can vastly improve an application’s performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for every project in 5 Java projects from DaCapo benchmark, 8 popular projects and 30 uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves all measures for 86% (37/43) of the projects. The median improvement across the best solutions is 4.8%, 10.1%, 5.1% for runtime, memory and CPU usage. These aggregate results understate ARTEMIS’s potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache’s XML transformation tool. ARTEMIS improves gson by 16.5%, 1% and 2.2% for memory, runtime, and CPU; ARTEMIS improves xalan’s memory consumption by 23.5%. Every client of these projects will benefit from these performance improvements.
| Type: | Proceedings paper |
|---|---|
| Title: | Darwinian Data Structure Selection |
| Event: | ESEC/FSE '18 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering |
| Location: | Lake Buena Vista, FL, USA |
| Dates: | 04 November 2018 - 10 November 2018 |
| ISBN-13: | 978-1-4503-5573-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3236024.3236043 |
| Publisher version: | https://doi.org/10.1145/3236024.3236043 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Search-based Software Engineering, Genetic Improvement, Software Analysis and Optimisation, Data Structure Optimisation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10062922 |
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