Empirical parallel performance prediction from Semantics-based profiling

Norman Scaife, Greg Michaelson, Susumu Horiguchi

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

1 Citation (Scopus)


The PMLS parallelizing compiler for Standard ML is based upon the automatic instantiation of algorithmic skeletons at sites of higher order function use. PMLS seeks to optimise run-time parallel behaviour by combining skeleton cost models with Structural Operational Semantics rule counts for HOF argument functions. In this paper, the formulation of a general rule count cost model as a set of over-determined linear equations is discussed, and their solution by singular value decomposition, and by a genetic algorithm, are presented. © Springer-Verlag Berlin Heidelberg 2005.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2005
Subtitle of host publication5th International Conference, Atlanta, GA, USA, May 22-25, 2005. Proceedings, Part II
Number of pages9
ISBN (Electronic)978-3-540-32114-9
Publication statusPublished - 2005
Event5th International Conference on Computational Science - Atlanta, GA, United States
Duration: 22 May 200525 May 2005

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference5th International Conference on Computational Science
Abbreviated titleICCS 2005
Country/TerritoryUnited States
CityAtlanta, GA


Dive into the research topics of 'Empirical parallel performance prediction from Semantics-based profiling'. Together they form a unique fingerprint.

Cite this