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Pipelined execution of data-parallel algorithms

Authors: Maksim Gorev; Raimund Ubar;

Pipelined execution of data-parallel algorithms

Abstract

A combination of pipelining and data-parallel execution on multiprocessor systems is proposed. The use of pipelining in coarse-grained data-parallel applications can be more advantageous, than the classical data-parallel approach. It is used in order to reduce redundant data transfers for all cores, involved in processing. Class of simulation applications is taken as an example to illustrate principles of the method. It is shown, that overall execution time could be reduced by significant amount of time required to transfer the model data. Set of experiments was carried out using a desktop multicore processor and OpenCL framework for parallel execution. Experimental results show that speedup is achievable even on general-purpose MPSoC platforms.

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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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