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We measure the execution time as user time, averaging the results over 100 runs.
First, we measure the execution time required to solve the optimal association problem using GAMS.
Finally, we benchmark our detector on embedded platforms to measure the execution time and the power consumption.
We varied the number of jobs issued by the user to measure the execution time required for detecting the frontier.
More precisely, we measure the execution time of both attacks (using prior knowledge on RSS feeds) on the TMN100 dataset.
The derived results are presented in Table 1; for simplicity, all time values are referenced to the BEEMD, to measure the execution time.
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For comparison purposes, we measured the execution times of broad-phase algorithms executing on a CPU and GPU.
For the CPU we measured the execution time when using 1, 2, 4 or 8 cores.
approach, we measured the execution times of the four versions using three different image sizes:,, and.
To evaluate this, we measured the execution time of all three algorithms.
We measured the execution speed of RAMAnalyzer when only a memory image was provided.
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CEO of Professional Science Editing for Scientists @ prosciediting.com