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In our benchmarking, we report mean computation times and memory measurements.
Table 2 shows the mean computation times to simulate and process output for the two dosing regimens shown in Figure 5 by three different methods (i.e., NONMEM® + R, uni-core Shiny, multi-core Shiny with compiled functions) when run four times each.
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The mean computation time per simulation was 7.8 min.
The mean computation time per frozen phonon for the computem simulations were 18.2 h resulting in a total computation time of 48.5 days.
The best, worst and mean values of the total production cost, together with the mean computation time by MIP, QEA, IBPSO and BGSO for different test systems are shown in Table 5.
The mean computation time for four mixture ratio estimators of the HTR distribution based on the proposed and the LM1, LM2, and FLOM methods is illustrated in Table 1 for different values of N=103,104,105,106 and α=1.5,γ=3.
Table 4 shows the mean computation time of the respective algorithms for aligning the network families in the NAPAbench datasets.
To compare the performances of various EAs, statistical measures like best, mean, standard deviation of results and average computation time, over 20 independent trials are considered.
Aside from the fact that fewer realizations means less computation time for the simulations, using 1600 realizations for both the six-cell and three-cell layouts means a comparison of the results between the two layouts is somewhat more statistically equitable.
We have seen this attractive phenomenon before in particle simulations, where it also meant that computation time was likely to dominate communication time.
This approach offers a means of reducing computation times.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com