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IEEE Software
Article . 2011 . Peer-reviewed
License: IEEE Copyright
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Joint Forces: From Multithreaded Programming to GPU Computing

Authors: Frank Feinbube; Peter Troger; Andreas Polze;

Joint Forces: From Multithreaded Programming to GPU Computing

Abstract

Using graphics hardware to enhance CPU-based standard desktop applications is a question not only of programming models but also of critical optimizations that are required to achieve true performance improvements. Two major hardware trends make parallel programming a crucial issue for all software engineers today: the rise of many-core CPU architectures and the inclusion of powerful graphics processing units (CPUs) in every desktop computer. Modern CPUs use the ever-increasing transistor count predicted by Moore's law not only for larger caches and improved prediction logic but also for adding more parallel execution units ("cores") per chip. Projections show that future processor generations will offer hundreds or even thousands of cores per socket. However, an application can only benefit from this development if it's prepared for parallel execution.

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    influence
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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!
7
Average
Top 10%
Top 10%
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