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This tool produces optimized results at a lower execution time for large bioassay data sets.
The proposed algorithm similar to Monte Carlo simulation achieves the same results; however with lower execution time.
In summary, for target accuracy, we should evaluate both two schemes and select the scheme that has lower execution time.
The MATLAB implementation has been compared with the MATLAB function funm on matrices of different dimensions, obtaining lower execution time and higher accuracy in most cases.
GPUs with a large number of computational cores and larger video RAM can run large bioassay data sets with significantly lower execution time.
This feature is enabled by compiling down to levels much below the machine code, which brings important additional effects: much lower execution time, equipment size, and power dissipation.
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Finally, the proposed algorithm exhibits both lower execution times and estimation errors compared to current noninvasive estimation techniques based on the use of the HT.
The results showed a reduction in performance (lower scores and longer execution time) proportional to the increase in the extent and syntactic complexity of the stimulus.
Based on the feedback interaction process, we can maintain a lower application execution time than other existing schemes.
Specifically, lower bounds on execution time independent of the data layout, lower bounds for data layouts in which the number of data items per processor is bounded, and lower bounds for specific data layouts commonly used in designing parallel algorithms for this problem are presented in this paper.
It can be seen from Table 3 that Ambric's device gives the highest performance, lowering the total execution time and achieving a high speed-up.
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