made the labels of plots work better with how they are presented in the results chapter

--HG--
rename : plots/scalability/speedup.plt => plots/scalability/efficiency.plt
This commit is contained in:
Stephen McQuay 2011-05-30 22:49:24 -06:00
parent 03e1e3a24a
commit f47585735d
14 changed files with 96 additions and 45 deletions

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@ -9,6 +9,7 @@ set key right bottom
set xrange [0.008:0.18] set xrange [0.008:0.18]
set xlabel 'Mesh Spacing'
set ylabel 'RMS of error' set ylabel 'RMS of error'
plot './resolution.2D.64.out' \ plot './resolution.2D.64.out' \

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@ -10,7 +10,7 @@ set key right bottom
set xrange [0.004:0.055] set xrange [0.004:0.055]
set xlabel 'Mesh Spacing' set xlabel 'Mesh Spacing'
set ylabel 'RMS of error' # set ylabel 'RMS of error'
plot './resolution.3D.gmsh.out' \ plot './resolution.3D.gmsh.out' \
u 1:2 t '{/Symbol n} = 2' w l,\ u 1:2 t '{/Symbol n} = 2' w l,\

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@ -0,0 +1,12 @@
load '../main.plt'
set output 'efficiency.eps'
set nokey
set ylabel "Efficiency (E_p)"
set xlabel "Number of Participating Minions"
set yrange [0:1]
plot 'scale.out' u 1:3 w lp

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@ -0,0 +1,20 @@
set terminal postscript enhanced eps color
set output "hist.eps"
set key off
# Make some suitable labels.
set title "Work Performed per Participant"
set xlabel "Number of Interpolations Performed"
set ylabel "Number of Minions"
set style histogram clustered gap 1
set style fill solid border -1
binwidth=5
set boxwidth binwidth
bin(x,width)=width*floor(x/width) + binwidth/2.0
plot 'hist1.out' using (bin($1,binwidth)):(1.0) smooth freq with boxes
# plot 'hist2.out' using (bin($1,binwidth)):(1.0) smooth freq with boxes

22
plots/scalability/hist.py Normal file
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@ -0,0 +1,22 @@
import sys
import shelve
import pickle
import numpy as np
if len(sys.argv) != 2:
print "usage: %s <interp.shelve>" % sys.argv[0]
sys.exit(1)
s = shelve.open(sys.argv[1])
d = dict(s)
s.close()
for line in (i[1] for i in sorted(d.iteritems(), key = lambda x: x[1]['stats']['participants'])):
run = line['stats']
print "#", run['participants']
x = np.array(run['tasks'].values())
for i in x:
print i
print

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@ -12,11 +12,15 @@ s.close()
for line in (i[1] for i in sorted(d.iteritems(), key = lambda x: x[1]['stats']['participants'])): for line in (i[1] for i in sorted(d.iteritems(), key = lambda x: x[1]['stats']['participants'])):
run = line['stats'] run = line['stats']
velocity = run['count'] / run['receive'] thruput = run['count'] / run['receive']
p = run['participants']
# speedup (http://en.wikipedia.org/wiki/Speedup): # speedup (http://en.wikipedia.org/wiki/Speedup):
S_p = run['receive']/run['count'] / 1148.02904 T_1 = 0.114802904391
S_p = T_1 / (run['receive']/run['count'])
print run['participants'], velocity, S_p E_p = S_p / p
print p, S_p, E_p

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@ -1,19 +1,19 @@
1 8.71058101972 0.000100000000341 1 0.999999999997 0.999999999997
2 16.9174102062 5.14888564103e-05 2 1.94216782645 0.971083913223
4 33.8342624632 2.57448527477e-05 4 3.8842715987 0.971067899674
5 41.369278595 2.10556754801e-05 5 4.74931333527 0.949862667054
8 70.8631618899 1.22921145728e-05 8 8.1352967993 ?1.01691209991
11 95.1311787574 9.15638927551e-06 11 10.9213356195 0.992848692681
16 140.277826152 6.20952098301e-06 16 16.1043018639 ?1.00651886649
22 188.945066816 4.6101129795e-06 22 21.6914424408 0.985974656402
32 275.671657764 3.15976662965e-06 32 31.6479069695 0.988997092798
45 387.071286152 2.25038161213e-06 45 44.4369078566 0.987486841258
64 550.492432027 1.58232530415e-06 64 63.198130042 0.987470781906
90 774.640026224 1.12446823744e-06 90 88.930924868 0.988121387422
128 1097.65200754 7.93564899404e-07 128 126.013638476 0.984481550592
181 1422.36034327 6.12403255659e-07 181 163.291098498 0.902160765184
196 1500.92599599 5.80347137214e-07 196 172.310663615 0.879136038854
224 1182.63366931 7.36540931943e-07 224 135.769780068 0.606115089587
256 1311.56362765 6.64137131117e-07 256 150.571313747 0.588169194325
384 1214.03638311 7.17489291971e-07 384 139.374902817 0.362955476086
512 1359.50184047 6.40718591923e-07 512 156.074759811 0.304833515255

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@ -1,3 +1,4 @@
8 2
16 7 16 7
45 30 45 30
64 40 64 40
@ -7,4 +8,5 @@
196 175 196 175
224 185 224 185
256 195 256 195
512 195 384 200
512 200

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@ -7,5 +7,6 @@ set nokey
set xlabel "Number of Participating Minions" set xlabel "Number of Participating Minions"
set ylabel "Approximate CPU utilization (% CPU)" set ylabel "Approximate CPU utilization (% CPU)"
# set log xy set yrange [0:210]
plot 'server.out' u 1:2 w lp t 'Speedup' plot 'server.out' u 1:2 w lp t 'Speedup'

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@ -4,8 +4,7 @@ set output 'speedup.eps'
set nokey set nokey
set xlabel "Number of Participating Minions"
set ylabel "Speedup (S_p)" set ylabel "Speedup (S_p)"
set xlabel "Number of Participating Minions"
set log xy plot 'scale.out' u 1:2 w lp t 'Speedup'
plot 'scale.out' u 1:3 w lp t 'Speedup'

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@ -1,10 +0,0 @@
load '../main.plt'
set output 'thruput.eps'
set nokey
set xlabel "Number of Participating Minions"
set ylabel "Interpolation Throughput (interpolations / second)"
plot 'scale.out' u 1:2 w lp t 'velocity'

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@ -3,7 +3,7 @@ load '../all.plt'
set output 'timing.2.aggregate.eps' set output 'timing.2.aggregate.eps'
set title '2-D Interpolation Timing Data' # set title '2-D Interpolation Timing Data'
plot 'time.out' u 1:2 t 'Order 2, res 1' w lp, \ plot 'time.out' u 1:2 t 'Order 2, res 1' w lp, \
'' u 1:3 t 'Order 3, res 1' w lp, \ '' u 1:3 t 'Order 3, res 1' w lp, \

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@ -5,7 +5,7 @@ set output 'timing.3.aggregate.eps'
unset ylabel unset ylabel
set title '3-D Interpolation Timing Data' # set title '3-D Interpolation Timing Data'
plot 'time.out' u 1:2 t 'Order 2, Mesh 1' w lp, \ plot 'time.out' u 1:2 t 'Order 2, Mesh 1' w lp, \
'' u 1:3 t 'Order 3, Mesh 1' w lp, \ '' u 1:3 t 'Order 3, Mesh 1' w lp, \

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@ -3,6 +3,6 @@ set ylabel "Percent of Improved Interpolations"
set log x set log x
set yrange [0:1] # set yrange [0:1]
set key bottom right set key bottom right