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This strategy is embedded in a Graphics Processing Unit (GPU) local search procedure, in order to take advantage of the highly parallel processors from this architecture.
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An overview of the testbed is given, including the analyzed algorithms, the digital signal processing with a new highly parallel processor to perform the algorithms in real time, as well as the analog front-ends.
ASC enables the use of FPGAs as highly parallel stream processors.
Modern general-purpose GPUs are not only powerful graphics engines but also highly parallel programmable processors.
Graphics Processing Units (GPUs) are highly parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized.
Thus, when supercomputers built with commercial operating systems around highly parallel arrays of standard processors tackle such challenges, they often perform at 5percentt of their rated speed or less.
Graphical Processing Units (GPUs) have evolved into highly parallel, multi-threaded, multicore powerful processors with high memory bandwidth.
In recent years the programmable commodity graphics processing unit (GPU) has evolved into a highly parallel, multi-threaded, many-core processor with tremendous computational speed and very high memory bandwidth.
On the one hand, MCE algorithms often create a backtracking search tree that has a highly irregular and hard-or-impossible to predict structure; therefore, almost any static decomposition of the search tree by parallel processors results in highly unbalanced processor execution times.
The scheduling is distributed — performed on the processors of a highly parallel computer architecture.
The architectures of programmable network processors are typically highly parallel and a single algorithm can be mapped in different ways onto the hardware.
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