performance section

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Julian M. Kunkel 2020-10-23 09:54:47 +01:00
parent f4c0f27aad
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@ -150,6 +150,9 @@ Finally, we conclude our paper in \Cref{sec:summary}.
Clustering of jobs based on their names Clustering of jobs based on their names
Multivariate time series
Levenshtein distance also known as Edit Distance (ED).
Vampir clustering of timelines of a single job. Vampir clustering of timelines of a single job.
\section{Methodology} \section{Methodology}
@ -328,10 +331,11 @@ The runtime is normalized for 100k jobs, i.e., for BIN\_all it takes about 41\,s
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 Levenshtein based algorithms take longer for longer jobs -- proportional to the job length as it applies a sliding window. The Levenshtein based algorithms take longer for longer jobs -- proportional to the job length as it applies a sliding window.
The KS algorithm is faster than the others by 10x but it operates on the statistics of the time series.
Note that the current algorithms are sequential and executed on just one core. Note that the current algorithms are sequential and executed on just one core.
For computing the similarity to one (or a small set of reference jobs), they could easily be parallelized. For computing the similarity to one (or a small set of reference jobs), they could easily be parallelized.
We believe this will then allow a near-online analysis of a job. We believe this will then allow a near-online analysis of a job.
\jk{To analyze KS jobs}
\begin{figure} \begin{figure}
\centering \centering

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@ -11,6 +11,9 @@ prefix = args[2]
# Plot the performance numbers of the analysis # Plot the performance numbers of the analysis
data = read.csv(file) data = read.csv(file)
levels(data$alg_name)[levels(data$alg_name) == "bin_aggzeros"] = "bin_aggz"
levels(data$alg_name)[levels(data$alg_name) == "hex_native"] = "hex_nat"
levels(data$alg_name)[levels(data$alg_name) == "hex_phases"] = "hex_phas"
e = data %>% filter(jobs_done >= (jobs_total - 9998)) e = data %>% filter(jobs_done >= (jobs_total - 9998))
e$time_per_100k = e$elapsed / (e$jobs_done / 100000) e$time_per_100k = e$elapsed / (e$jobs_done / 100000)