Algorithm + Strategy = Parallelism

Algorithm + Strategy = Parallelism (1998)

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by P.W. Trinder , K. Hammond , H.-W. Loidl , S.L. Peyton Jones

Venue:	JOURNAL OF FUNCTIONAL PROGRAMMING
Citations:	51 - 18 self

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Datum	Value	Source
TITLE	Algorithm + Strategy = Parallelism	INFERENCE
AUTHOR NAME	P. W. Trinder	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computing Science, University of Glasgow, Glasgow	SVM HeaderParse 0.2
AUTHOR ADDR	UK	SVM HeaderParse 0.1
AUTHOR NAME	K. Hammond	SVM HeaderParse 0.1
AUTHOR AFFIL	Division of Computing Science	SVM HeaderParse 0.2
AUTHOR ADDR	University of St Andrews, St Andrews, UK	SVM HeaderParse 0.1
AUTHOR NAME	H. -w. Loidl	SVM HeaderParse 0.1
AUTHOR AFFIL	y; Department of Computing Science, University of Glasgow, Glasgow	SVM HeaderParse 0.2
AUTHOR ADDR	UK	SVM HeaderParse 0.1
AUTHOR NAME	S. L	SVM HeaderParse 0.1
AUTHOR AFFIL	y; Department of Computing Science, University of Glasgow, Glasgow	SVM HeaderParse 0.2
AUTHOR ADDR	UK	SVM HeaderParse 0.1
AUTHOR NAME	Peyton Jones	SVM HeaderParse 0.1
AUTHOR AFFIL	y; Department of Computing Science, University of Glasgow, Glasgow	SVM HeaderParse 0.2
AUTHOR ADDR	UK	SVM HeaderParse 0.1
ABSTRACT	The process of writing large parallel programs is complicated by the need to specify both the parallel behaviour of the program and the algorithm that is to be used to compute its result. This paper introduces evaluation strategies, lazy higher-order functions that control the parallel evaluation of non-strict functional languages. Using evaluation strategies, it is possible to achieve a clean separation between algorithmic and behavioural code. The result is enhanced clarity and shorter parallel programs. Evaluation strategies are a very general concept: this paper shows how they can be used to model a wide range of commonly used programming paradigms, including divideand-conquer, pipeline parallelism, producer/consumer parallelism, and data-oriented parallelism. Because they are based on unrestricted higher-order functions, they can also capture irregular parallel structures. Evaluation strategies are not just of theoretical interest: they have evolved out of our experience in parallelising several large-scale parallel applications, where they have proved invaluable in helping to manage the complexities of parallel behaviour. These applications are described in detail here. The largest application we have studied to date, Lolita, is a 60,000 line natural language parser. Initial results show that for these programs we can achieve acceptable parallel performance, while incurring minimal overhead for using evaluation strategies. 1 Writing Parallel Programs While it is hard to write good sequential programs, it can be considerably harder to write good parallel ones. At Glasgow we have worked on several fairly large parallel programming projects and have slowly, and sometimes painfully, developed a methodology for parallelising sequential programs. The essence of the problem facing the parallel programmer is that, in addition to specifying what value the program should compute, explicitly parallel programs	SVM HeaderParse 0.1
YEAR	1998	INFERENCE
VENUE	Journal of Functional Programming	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	23--60	INFERENCE
VOLUME	8	INFERENCE
CITATIONS	45 found	ParsCit 1.0