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Both of these computing architectures reduce computation time by exploiting the parallelism inherent in the application.
We also show how to reduce computation time on computers with a cache-based architecture.
The use of parallel computing could drastically reduce computation time for MDR, particularly on a cluster machine.
Parallel computing is a powerful way to reduce computation time and to improve the quality of solutions of evolutionary algorithms (EAs).
Second, it must have low computational cost since we have to reduce computation time to a clinically acceptable level.
Its major advantage is to considerably reduce computation time.
Within hydrological models, flow approximations are commonly used to reduce computation time.
The compression level was set to optimum level (Zhang et al. 2010) to reduce computation time.
This involves optimizing our system architecture to further reduce computation time and memory footprint.
This hybrid approach was able to reduce computation time by some orders of magnitude.
We use offline initialization to reduce computation time for the solution of the optimization problem.
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