Compilation of functional languages using flow graph analysis

Hartel, Pieter H.; Glaser, Hugh; Wild, J. Martin

doi:10.1002/spe.4380240202

Cited by 15 publications

(13 citation statements)

References 23 publications

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“…The function gh computes the water heights. This function is the implementation of equation (6). The height and velocity values are stored in three separate matrices, so that each of the three functions works on its own matrix.…”

Section: Data Distribution Based On the Farm Approachmentioning

confidence: 99%

“…The FAST compiler [6] together with the FCG code generator [7] implement Intermediate, which is a subset of Miranda. To allow Miranda programs to be compiled by ihe FASTFCG compiler, three restrictions must be observed;…”

Section: The Fast/fcg Compilermentioning

confidence: 99%

“…Although the interpreter does not allow for efficient execution of the program, the development cycle is short when using small input data sets because there is no compilation phase. When the programmer is satisfied that the program is working, the program is compiled using the FAST/FCG [6,7] compiler for Intermediate. This compiler translates the program into an equivalent C program, which is compiled to the target platform using a C compiler (resulting in code that is portable to any machine with a C compiler).…”

Section: Program Development Cyclementioning

confidence: 99%

“…Section 3 describes how the toolkit supports the development of parallel functional programs. The various parts of the toolkit are highlighted in Sections [4][5][6][7]. A case study, Section 8, presents two implementations of an algorithm to predict the tidal flow in an estuary of the North sea.…”

Section: Introductionmentioning

confidence: 99%

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A toolkit for parallel functional programming

Hartel

Hofman

Langendoen

et al. 1995

Concurrency: Pract. Exper.

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SUMMARYOur toolkit for the design and implementation of parallel functional programs supports the stepwise development of parallel programs from a high level sequential specification to a n optimised parallel implementation. The toolkit is used as follows:The algorithm to be implemented is specified in a functional language. The program is debugged and tested using an interpreter.The program is compiled for a sequential machine. Its performance is analysed and improved. Annotation-driven transformations are applied to the program to indicate parallel tasks. Simulations at task level, basic block level and bus transaction level make it possible to analyse the parallel performance of the program at three levels of detail. When the performance is optimised using the simulators, the program is executed on a genuine parallel machine.Several programs have been developed with the toolkit. A program that simulates tidal flow in a n estuary of the North sea is presented as a case study to demonstrate the merits of the toolkit when developing complex parallel programs.The toolkit not only supports the design of parallel applications, it also allows the study of important concepts in parallel computer architecture. These include the behaviour of cached memory systems, bus protocols, scheduling algorithms and memory management algorithms.

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Section: Data Distribution Based On the Farm Approachmentioning

confidence: 99%

Section: The Fast/fcg Compilermentioning

confidence: 99%

Section: Program Development Cyclementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A toolkit for parallel functional programming

Hartel

Hofman

Langendoen

et al. 1995

Concurrency: Pract. Exper.

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“…To compile the Miranda code we use our own FAST compiler [26]. This compiler offers efficient array primitives.…”

Section: Performance Assessmentmentioning

confidence: 99%

Declarative solutions to partitioned-grid problems

Etalle¹,

Hartel²,

Vree³

1999

Softw: Pract. Exper.

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The problem of partitioning grid-based applications for parallel computing can be solved easily and intuitively in a logic programming language such as Prolog, using only the single assignment property of the logic variable, and not the backtracking. We show that such a logic program can be transformed in a systematic way into a circular functional program, which runs 10 times faster than the original logic program. The transformation proceeds in a number of steps. The first step is novel, and we give a correctness proof. Our reasoning also uses a novel combination of concepts from both the logical and functional paradigms. [3] is a logic programming language which achieves a remarkable execution speed by exploiting mode and types information of the program.

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