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An algorithm is devised to reduce the computing time by using a vector computer.
The purpose of this paper is to reduce the computing time of this coupled field on the premise of guaranteeing computational accuracy, thus improving its practicability.
Super computers and parallel computing techniques are some promising tools to reduce the computing time and to achieve better solutions, especially when solving large-scale problems.
Grid coarsening is the straightforward way to reduce the computing efforts for 2D flood modelling.
To reduce the computing complexity the Inversion model can be further simplified to a GMM.
The second approach determines the early termination conditions to reduce the computing time [9 15].
This is benefitted from more processing parallelism provided by SmartCell to reduce the computing complexity.
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Hence, we can also use the integral image data structure to reduce the computational cost of computing local histograms needed by AHE.
To reduce the computational cost, we compute the dynamics of the coarse grid displacement and relate it to the mean displacement.
In addition, the GPU-based parallel computation is shown to significantly reduce the computing time.
The aim is to drastically reduce the computing efforts required for the simulation and therefore allow its use for the geometrical tolerances allocation optimisation.
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