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Therefore, it can improve the computing efficiency while preserving the key properties of the network.
An optimization is also developed to improve the computing efficiency of the path generation process.
Instead of the conventional 2D block matching, we employ the parallel multi-1D blocks matching to improve the computing speed.
The objective of this paper is to improve the computing time for numerical analysis of particle charging process by using discrete element method.
A detailed deliberation of how the processor micro-architecture as well as the manual optimization techniques improve the computing performance has been developed.
In addition, we have to utilize the parallel computing techniques on Linux clusters, which are widely available for academic and industrial applications nowadays, to improve the computing efficiency.
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To improve the compute speed, SVEngine is highly parallelized to reduce the simulation time.Conclusions: We demonstrated the versatile features of SVEngine and its improved runtime comparisons with other available simulators.
In many cases, however, judicious use of preexisting manually optimized IP blocks within these datapaths could improve the compute performance even further.
Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision.
Through the comparison of three methods (Monte Carlo method, DNNM, PSO/BR-DNNM), it is demonstrated that PSO/BR-DNNM reshapes the probability of flexible mechanism probabilistic analysis and improves the computing efficiency while keeping acceptable computational precision.
Combining message passing interface (MPI) with FDTD to realize parallel computation has been proven to be an efficient way in improving the computing speed.
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