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 \emph{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 \emph{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 $23.5$%. \emph{Every} client of these projects will benefit from these performance improvements.
Tue 6 NovDisplayed time zone: Guadalajara, Mexico City, Monterrey change
13:30 - 15:00 | Software Analysis IJournal-First / Research Papers at Horizons 5 Chair(s): Sebastian Elbaum University of Nebraska-Lincoln, USA | ||
13:30 22mTalk | On Accelerating Source Code Analysis At Massive Scale Journal-First DOI | ||
13:52 22mTalk | RefiNym: Using Names to Refine Types Research Papers Santanu Dash University College London, UK, Miltiadis Allamanis Microsoft Research, Cambridge, Earl T. Barr | ||
14:15 22mTalk | Darwinian Data Structure Selection Research Papers Michail Basios University College London, Lingbo Li University College London, UK, Fan Wu University College London, UK, Leslie Kanthan University College London, UK, Earl T. Barr DOI Pre-print | ||
14:37 22mTalk | Scalability-First Pointer Analysis with Self-Tuning Context-Sensitivity Research Papers Yue Li Aarhus University, Denmark, Tian Tan Aarhus University, Denmark, Anders Møller Aarhus University, Yannis Smaragdakis University of Athens |