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Data parallelism and Linda

Authors: Nicholas Carriero; David Gelernter;

Data parallelism and Linda

Abstract

Is the owner-computes style of parallelism, captured in a variety of data parallel languages, attractive as a paradigm for designing explicitly parallel codes? This question gives rise to a number of others. Will such use be unwieldy? Will the resulting code run well? What can such an approach offer beyond merely replicating, in a more labor intensive way, the services and coverage of data parallel languages? We investigate these questions via a simple example and a “real world” case study developed using C-Linda, an explicit parallel programming language formed by the merger of C with the Linda 1 coordination language. The results demonstrate owner-computes is an effective design strategy in Linda.

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citations
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!
3
Average
Average
Average
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