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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.
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An optimization is also developed to improve the computing efficiency of the path generation process.
Therefore, it can improve the computing efficiency while preserving the key properties of the network.
Instead of the conventional 2D block matching, we employ the parallel multi-1D blocks matching to improve the computing speed.
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.
The massive data of vibration measurement can be processed concurrently in the VMG, which can improve the computing efficiency and reduce the cost.
A detailed deliberation of how the processor micro-architecture as well as the manual optimization techniques improve the computing performance has been developed.
In order to improve the computing efficiency, the single program multiple data (SPMD) model parallel acceleration technology is adopted, according to the characteristic of the current grid system.
After some validations, it was found that MRANS can successfully keep the accuracy and improve the computing efficiency by nearly two times.
The objective of this paper is to improve the computing time for numerical analysis of particle charging process by using discrete element method.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com