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Utilising Compute Unified Device Architecture (CUDA) platform for Graphics Processing Units (GPUs) enables significant reduction of computation time at a moderate cost, by means of parallel computing.
The numerical model was based on a robust 1D geometry which resulted in reduction of computation time.
The results show that the combined genetic algorithm-subregion method has good convergence with a substantial reduction of computation time.
A reduction of computation time up to 50% has been observed for moderate number of points and rank of tensors.
The application of time and amplitude similarity with KNN improved the accuracy of adaptive threshold as well as the reduction of computation time.
This new approach has been shown to improve not only on the accuracy of the MLE procedure, but additionally results in a reduction of computation time.
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Implementations of the methods are available both for MATLAB and FORTRAN (the latter allows a significant reduction of computation times).
Further decrease of computation time is shown in Figure 3a.
A reduction by about 50% of computation time could be obtained, without significant changes in the estimation performance and in the clinical results.
While a traditional R package cannot compete with Java or C++ in terms of computation time, reducing computation time is possible.
This may lead to the reduction of the total computing time, provided that the decrement of communication time is not less than the increment of computation time for the inner iterations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com