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Fix Color map for job vis.

master
Julian M. Kunkel 3 years ago
parent
commit
b71a0a26ef
  1. BIN
      fig/job-timeseries4296426.pdf
  2. BIN
      fig/job-timeseries5024292.pdf
  3. BIN
      fig/job-timeseries7488914-30.pdf
  4. BIN
      fig/job-timeseries7488914.pdf
  5. 42
      paper/main.tex
  6. 13
      scripts/create-paper-vis.sh
  7. 25
      scripts/plot-single-job.py
  8. 2
      scripts/plot.R

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fig/job-timeseries4296426.pdf

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fig/job-timeseries5024292.pdf

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fig/job-timeseries7488914-30.pdf

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fig/job-timeseries7488914.pdf

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42
paper/main.tex

@ -44,7 +44,8 @@
\usepackage{graphicx}
\graphicspath{
{./pictures/}
{./pictures/},
{../fig/}
}
\usepackage[backend=bibtex, style=numeric]{biblatex}
@ -127,8 +128,9 @@ Check time series algorithms:
\begin{itemize}
\item bin
\item hex\_native/hex\_lev
\item pm\_quant
\item hex\_native
\item hex\_lev
\item hex\_quant
\end{itemize}
\section{Evaluation}
@ -136,8 +138,9 @@ Check time series algorithms:
Two study examples (two reference jobs):
\begin{itemize}
\item jobA: shorter length, e.g. 5-10, that has a little bit IO in at least two metadata metrics (more better).
\item jobB: a very IO intensive longer job, e.g., length $>$ 20, with IO read or write and maybe one other metrics.
\item job-short: shorter length, e.g. 5-10, that has a little bit IO in at least two metadata metrics (more better).
\item job-mixed:
\item job-long: a very IO intensive longer job, e.g., length $>$ 20, with IO read or write and maybe one other metrics.
\end{itemize}
For each reference job: create CSV file which contains all jobs with:
@ -151,6 +154,35 @@ Insert job profiles for closest 10 jobs.
Potentially, analyze how the rankings of different similarities look like.
\Cref{fig:refJobs}
\begin{figure}
\begin{subfigure}{0.8\textwidth}
\includegraphics[width=\textwidth]{job-timeseries4296426}
\caption{Job-S} \label{fig:job-S}
\end{subfigure}
\caption{Reference jobs: timeline of mean IO activity}
\label{fig:refJobs}
\end{figure}
\begin{figure}\ContinuedFloat
\begin{subfigure}{0.8\textwidth}
\includegraphics[width=\textwidth]{job-timeseries5024292}
\caption{Job-M} \label{fig:job-M}
\end{subfigure}
\begin{subfigure}{0.8\textwidth}
\includegraphics[width=\textwidth]{job-timeseries7488914-30.pdf}
\caption{Job-L (first 30 segments of 400; remaining segments are similar)}
\label{fig:job-L}
\end{subfigure}
\caption{Reference jobs: timeline of mean IO activity; non-shown timelines are 0}
\end{figure}
\section{Summary and Conclusion}
\label{sec:summary}

13
scripts/create-paper-vis.sh

@ -0,0 +1,13 @@
#!/bin/bash
# This script calls all other scripts to re-create the figures for the paper
mkdir fig
for job in 5024292 4296426 7488914 ; do
./scripts/plot-single-job.py $job "fig/job-"
done
for file in fig/*.pdf ; do
pdfcrop $file output.pdf
mv output.pdf $file
done

25
scripts/plot-single-job.py

@ -5,12 +5,25 @@ import sys
from pandas import DataFrame
from pandas import Grouper
from matplotlib import pyplot
import matplotlib.cm as cm
jobs = [sys.argv[1]]
prefix = sys.argv[2]
print("Plotting the job: " + str(jobs))
# Color map
colorMap = { "md_file_create": cm.tab10(0),
"md_file_delete": cm.tab10(1),
"md_mod": cm.tab10(2),
"md_other": cm.tab10(3),
"md_read": cm.tab10(4),
"read_bytes": cm.tab10(5),
"read_calls": cm.tab10(6),
"write_bytes": cm.tab10(7),
"write_calls": cm.tab10(8)
}
# Plot the timeseries
def plot(prefix, header, row):
x = { h : d for (h, d) in zip(header, row)}
@ -36,27 +49,31 @@ def plot(prefix, header, row):
groups = data.groupby(["metrics"])
metrics = DataFrame()
labels = []
colors = []
for name, group in groups:
metrics[name] = [x[2] for x in group.values]
labels.append(name)
colors.append(colorMap[name])
fsize = (8, 1 + 1.5 * len(labels))
ax = metrics.plot(subplots=True, legend=False, sharex=True, grid = True, sharey=True, colormap='jet', marker='.', markersize=10, figsize=(8, 2 + 2 * len(labels)))
ax = metrics.plot(subplots=True, legend=False, sharex=True, grid = True, sharey=True, marker='.', markersize=10, figsize=fsize, color=colors)
for (i, l) in zip(range(0, len(labels)), labels):
ax[i].set_ylabel(l)
pyplot.xlabel("Segment number")
pyplot.savefig(prefix + "timeseries" + jobid + ".png")
pyplot.savefig(prefix + "timeseries" + jobid + ".pdf")
# Plot first 30 segments
if len(timeseries) <= 50:
return
ax = metrics.plot(subplots=True, legend=False, sharex=True, grid = True, sharey=True, colormap='jet', marker='.', markersize=10, xlim=(0,30))
ax = metrics.plot(subplots=True, legend=False, sharex=True, grid = True, sharey=True, marker='.', color=colors, markersize=10, xlim=(0,30), figsize=fsize)
for (i, l) in zip(range(0, len(labels)), labels):
ax[i].set_ylabel(l)
pyplot.xlabel("Segment number")
pyplot.savefig(prefix + "timeseries" + jobid + "-30.png")
pyplot.savefig(prefix + "timeseries" + jobid + "-30.pdf")
### end plotting function

2
scripts/plot.R

@ -34,7 +34,7 @@ print(summary(e))
ggsave("ecdf-0.5.png")
# histogram for the jobs
ggplot(data, aes(similarity), group=alg_name) + geom_histogram(color="black", binwidth=0.025) + aes(fill = alg_name) + facet_grid(alg_name ~ ., switch = 'y') + scale_y_continuous(limits=c(0, 100), oob=squish) + scale_color_brewer(palette = "Set2") + ylab("Count (cropped at 100)")
ggplot(data, aes(similarity), group=alg_name) + geom_histogram(color="black", binwidth=0.025) + aes(fill = alg_name) + facet_grid(alg_name ~ ., switch = 'y') + scale_y_continuous(limits=c(0, 100), oob=squish) + scale_color_brewer(palette = "Set2") + ylab("Count (cropped at 100)") + theme(legend.position = "none")
ggsave("hist-sim.png")
# load job information, i.e., the time series per job

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