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The possibility to reduce the execution time using parallel computing is discussed.
Two strategies of parallelization are implemented in order to reduce the execution time.
At system level, these gains reduce the execution time of the benchmarks considered.
The techniques shown in the book significantly reduce the execution time incurred by deformable registration algorithms.
This paper will detail the development of a distributed chromosome GA to reduce the execution time of GARP.
When run on a multi-processor system, many of the tasks can be run in parallel to reduce the execution time of the script.
Experimental results have confirmed that the proposed approach can reduce the execution time by 45% when compared to other existing methods.
The use of multiple unit controllers for parallel processing of multi-unit motor drive systems can significantly reduce the execution time of the control algorithm.
Benchmark results show that a well-designed load balancing simulation can reduce the execution time up to an order of magnitude.
To reduce the execution cost of MBFL technique, this paper proposes a dynamic mutation execution strategy (DMES) to prioritize the execution on both mutants and test cases.
The experimental results reflect that the GPU-Jaya algorithm can accurately estimate battery model parameters while tremendously reduce the execution time using both entry-level and professional GPUs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com