Empirical parallel performance prediction from semantics-based profiling

Authors:
Norman Scaife;Greg Michaelson;Susumu Horiguchi
Affiliations:
Centre Equation, VERIMAG, Giers, France;School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, Scotland;Department of Computer Science, Graduate School of Information Sciences, Tohoku University, Sendai, Japan
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Year:
2005

Citing 4
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Implementing recursive functions as processor farms

Parallel Computing
The Definition of Standard ML

The Definition of Standard ML
Skeleton realisations from functional prototypes

Patterns and skeletons for parallel and distributed computing
Discovering applications of higher order functions through proof planning

Formal Aspects of Computing

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Abstract

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 be- haviour 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 decom- position, and by a genetic algorithm, are presented.