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Due to dramatic increases in available computing power and concomitant decreases in computing costs over the last decade, learning or mining by applying parallel processing techniques has become a feasible way to overcome the slow-learning problem.
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The stream is processed by one or more kernel functions, each of which is a compute-intensive inner loop that applies parallel processing to a stream that is resident in on-chip memory.
In this context, it seems that Big Data processing must profit from available computing resources by applying parallel execution models, thereby achieving results in "acceptable" times.
The overall performance is increased by 50% when applying parallel programming.
This can be achieved by use of parallel processing architect in place of series processing [31, 32].
Data were obtained by applying different image processing strategies [ 46].
Full search ME designs handle the computational complexity by using parallel processing elements for matching cost computation [10].
Supercomputers can handle this complexity by leveraging parallel processing architectures, in which thousands of CPUs and GPUs are coordinated to run similar processes at the same time.
Furthermore, by leveraging parallel processing, readDepth produces results many times faster than comparable algorithms.
However, the whammy effect may also be explained by the parallel processing account simply due to the competition between phonemes.
The required processing time could be significantly reduced by parallel processing on multicore computers [ 44] or by taking advantage of the speed of graphics processor units [ 45].
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