- author = "Oliver Krauss",
- title = "Towards a Framework for Stochastic Performance Optimizations in Compilers and Interpreters - An Architecture Overview",
- booktitle = "15th International Conference on Managed Languages and Runtimes (ManLang'18)",
- year = "2018",
- editor = "Eli Tilevich",
- address = "JKU Linz, Austria",
- month = sep # " 12-14",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, genetic improvement, Performance Optimization, Stochastic Optimization, Pattern Mining, AST Transformation",
- isbn13 = "9781450364249",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Krauss_2018_ManLang.pdf",
-
URL = "
http://ssw.jku.at/manlang18/index.php/manlang/program",
-
DOI = "
doi:10.1145/3237009.3237024",
- size = "7 pages",
-
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 using 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.",
- notes = "http://ssw.jku.at/manlang18/",
