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Eugen Betke 2020-08-26 16:33:47 +02:00
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@ -200,7 +200,7 @@ For example, we can see in \Cref{fig:job-S}, that several metrics increase in Se
To measure the performance for computing the similarity to the reference jobs, the algorithms are executed 10 times on a compute node at DKRZ. To measure the performance for computing the similarity to the reference jobs, the algorithms are executed 10 times on a compute node at DKRZ.
A boxplot for the runtimes is shown in \Cref{fig:performance}. A boxplot for the runtimes is shown in \Cref{fig:performance}.
The runtime is normalized for 100k seconds, i.e., for bin\_all it takes about 41\,s to process 100k jobs out of the 500k total jobs that this algorithm will process. The runtime is normalized for 100k jobs, i.e., for bin\_all it takes about 41\,s to process 100k jobs out of the 500k total jobs that this algorithm will process.
Generally, the bin algorithms are fastest, while the hex algorithms take often 4-5x as long. Generally, the bin algorithms are fastest, while the hex algorithms take often 4-5x as long.
Hex\_phases is slow for Job-S and Job-M while it is fast for Job-L, the reason is that just one phase is extracted for Job-L. Hex\_phases is slow for Job-S and Job-M while it is fast for Job-L, the reason is that just one phase is extracted for Job-L.
The Levensthein based algorithms take longer for longer jobs -- proportional to the job length as it applies a sliding window. The Levensthein based algorithms take longer for longer jobs -- proportional to the job length as it applies a sliding window.