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On the parallelism of data

Authors: C.V. Papadopoulos;

On the parallelism of data

Abstract

This article presents a data-parallel language, which has been designed around the concepts of relations and reduction operations. Many parallel machines provide hardware support for reduction operations (such as summing all elements of an array), and these operations are widely used in parallel scientific computing. My claim is that a data-parallel language can be created by coupling well-tested ideas on relations with well-tested ideas on reduction operations. This article contains a description of a data-parallel extension of C based on these ideas. The extension, called UC, has been used for a variety of applications. This article expands on its central thesis, describes UC and presents performance measurements.

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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!
0
Average
Average
Average
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