From c66723cfb0407e6e39d2f8ad320cb11edc8fa738 Mon Sep 17 00:00:00 2001 From: "Julian M. Kunkel" Date: Fri, 23 Oct 2020 09:54:47 +0100 Subject: [PATCH] performance section --- fig/progress_4296426-out-boxplot.png | Bin 70265 -> 66691 bytes fig/progress_4296426-out-cummulative.png | Bin 91914 -> 168500 bytes fig/progress_5024292-out-boxplot.png | Bin 76823 -> 72402 bytes fig/progress_5024292-out-cummulative.png | Bin 112008 -> 207913 bytes fig/progress_7488914-out-boxplot.png | Bin 75045 -> 73041 bytes fig/progress_7488914-out-cummulative.png | Bin 91220 -> 145927 bytes paper/main.tex | 6 +++++- scripts/plot-performance.R | 3 +++ 8 files changed, 8 insertions(+), 1 deletion(-) diff --git a/fig/progress_4296426-out-boxplot.png b/fig/progress_4296426-out-boxplot.png index 44bf303f52338e2ffcde8bbf617b4357349a3f0b..8ad3765162456bca74671f45333f0e2fb1233ce4 100644 GIT binary patch literal 66691 zcmd?Rc{r8*`!>3orNOEwDWsAylqR!?$~;enD9IcdGE+2&RHl?MmU)guhEf#DESW1) zrp)uS&)f5S-}m<(dmsCF-@X6Y!#a-VQCXJz{@mB+y3X^w&g=6~N#Xo9Y6fZ&iL~wF 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b/paper/main.tex index ea83c0a..69b4818 100644 --- a/paper/main.tex +++ b/paper/main.tex @@ -150,6 +150,9 @@ Finally, we conclude our paper in \Cref{sec:summary}. Clustering of jobs based on their names +Multivariate time series +Levenshtein distance also known as Edit Distance (ED). + Vampir clustering of timelines of a single job. \section{Methodology} @@ -328,10 +331,11 @@ The runtime is normalized for 100k jobs, i.e., for BIN\_all it takes about 41\,s Generally, the bin algorithms are fastest, while the hex algorithms take often 4-5x as long. Hex\_phases is slow for Job-S and Job-M while it is fast for Job-L, the reason is that just one phase is extracted for Job-L. The Levenshtein based algorithms take longer for longer jobs -- proportional to the job length as it applies a sliding window. +The KS algorithm is faster than the others by 10x but it operates on the statistics of the time series. + Note that the current algorithms are sequential and executed on just one core. For computing the similarity to one (or a small set of reference jobs), they could easily be parallelized. We believe this will then allow a near-online analysis of a job. -\jk{To analyze KS jobs} \begin{figure} \centering diff --git a/scripts/plot-performance.R b/scripts/plot-performance.R index 4433b4b..6186095 100755 --- a/scripts/plot-performance.R +++ b/scripts/plot-performance.R @@ -11,6 +11,9 @@ prefix = args[2] # Plot the performance numbers of the analysis data = read.csv(file) +levels(data$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$alg_name) == "hex_phases"] = "hex_phas" e = data %>% filter(jobs_done >= (jobs_total - 9998)) e$time_per_100k = e$elapsed / (e$jobs_done / 100000)

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