Towards a framework for stochastic performance optimizations in compilers and interpreters - An architecture overview

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

Abstract

Modern compilers and interpreters provide code optimizations before and during run-time to stay competitive with alternative execution environments, thus moving required domain knowledge about the compilation process away from the developer and speeding up resulting software. These optimizations are often based on formal proof, or alternatively have recovery paths as backup. This publication proposes an architecture utilizing abstract syntax trees (ASTs) to optimize the runtime performance of code with stochastic - search based - machine learning techniques. From these AST modifying optimizations a pattern mining approach attempts to find transformation patterns which are applicable to a software language. The application of these patterns happens during the parsing process or the programs run-time. Future work consists of implementing and extending the presented architecture, with a considerable focus on the mining of transformation patterns.

Original languageEnglish
Title of host publicationProceedings of the 15th International Conference on Managed Languages and Runtimes, ManLang 2018 (formerly PPPJ)
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450364249
DOIs
Publication statusPublished - 12 Sep 2018
Event15th International Conference on Managed Languages and Runtimes, ManLang 2018 - Linz, Austria
Duration: 12 Sep 201813 Sep 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference15th International Conference on Managed Languages and Runtimes, ManLang 2018
CountryAustria
CityLinz
Period12.09.201813.09.2018

Keywords

  • AST Transformation
  • Pattern Mining
  • Performance Optimization
  • Stochastic Optimization

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