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The results obtained are discussed in terms of graphical processing unit limitations.
Introduction of Graphical Processing Units (GPUs) and computing using GPUs in recent years opened possibilities for simple parallelization of programs.
Clusters of graphical processing units are used to accelerate the computation of a time dependent relativistic wavefunction in an arbitrary external potential.
The classifiers have been implemented in CUDA/C++ to exploit the parallel architecture of graphical processing units (GPUs) and is freely available in a public repository.
The test results indicate that the number of threads has a great influence on the performance of graphical processing unit (GPU) based implementation.
The kernelised nature of the PRW and RASCAL algorithms allied with the ensemble approach lends itself to a parallel implementation exploiting the massively parallel processing capability of Graphical Processing Units (GPUs).
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By implementation of the algorithm on graphical processing units, 30 the mean registration time between pre- and postoperative images was 2 minutes, 55 seconds.
Implementation of the technique on graphical processing units reduced computation time to under 3 minutes, well within the required timescale, while retaining an accurate result.
We present a scheme for the parallelization of quantum Monte Carlo method on graphical processing units, focusing on variational Monte Carlo simulation of bosonic systems.
The computation time of D-LBTE solvers might be further reduced in the future by implementation on graphical processing units and additional refinements.
The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models.
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