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A Software Development Methodology for Parallel Processing Systems.

Authors: Thaddeus J. Thigpen; Sun I. Paek; Pranshu K. Gupta; Doc-Hwan Bae; Stephen S. Yau;

A Software Development Methodology for Parallel Processing Systems.

Abstract

Abstract : A software development framework for parallel processing systems based on the parallel object-oriented functional computation model PROOF is evaluated. PROOF/L, a C++ based programming language with additional parallel constructs required by PROOF, is extended to include array data type and input/output features to make PROOFIL easier to use in developing software for parallel processing systems. The front-end translator from PROOF/L to the intermediate form IFl, and the back-end translators from IFl to the C languages on two different MIMD parallel machines, nCube and KSR, are developed. Our framework is evaluated by comparing it with existing software development approaches for parallel processing systems. Our framework is suitable for large-scale parallel software development because it supports the concepts of hierarchical design and shared data, and frees the software developer from considering explicit synchronization, communication, and parallelism. The software development efforts using our framework can be greatly reduced due to implicit synchronization and communication and the compactness of PROOF/L programs. The extension of PROOF/L and the integration of PROOF/L with other programming languages to utilize existing library functions written in languages such as C and FORTRAN are also discussed.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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