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In comparison, commercially available graphics processing units (GPUs) are optimized for parallel data processing, and have recently been adapted to accelerate processing speeds of computationally intensive algorithms such as spectrometer-based FD-OCT [ 7– 12].
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In contrast, new exercise regimes tend to accelerate processing speed and improve attention and memory in all kinds of activities.
When the architecture of DSP is fixed, an effective way to accelerate processing speed is to use or develop new processing methods such as parallel and distributed algorithms.
State-of-the-art general purpose graphic processing units (GPGPU) were utilized to accelerate the processing.
Hence, within the sample-processing thread, several sub-threads are created to accelerate the processing.
A company called Aspera has now announced an alternative protocol designed to accelerate wireless transfer speeds.
The proposed architecture exploits the parallelism in the MC algorithm to accelerate the processing speed and uses the dedicated design to optimize memory accesses.
To enable the Smith-Waterman algorithm to produce exact results in a reasonably shorter time, much research has been focusing on using various high-performance architectures to accelerate the processing speed of the algorithm [ 10– 27].
MRN accelerates processing by recruiting BLM to the end.
This accelerated processing speed is possible because the proposed method replaces an iterative optimization by linear addition and multiplication operations.
It was therefore necessary to accelerate quickly through these speeds.
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