merge

gmsh/analyze_gmsh_output.py

@@ -11,10 +11,10 @@ orders = [2,3,4,5]
 extras = [4,6,8,12, 16,20,32,48, 64,96,128,192, 256]
 
 params = (
-           (2,32),
+           (2,64),
-           (3,48),
+           (3,64),
            (4,64),
-           (5,96),
+           (5,64),
          )
 
 if __name__ == '__main__':

plots/analyze.py

@@ -0,0 +1,25 @@
+import sys
+import shelve
+
+
+s = shelve.open(sys.argv[1])
+d = dict(s)
+s.close()
+
+# {'count': 10000, 'extra': 256, 'receive': 1148.0290439128876, 'submit': 4.298105001449585, 'participants': 1, 'order': 5}
+# 'tasks':  defaultdict(<type 'int'>, {'m5-2-1.local-31671': 10000}),
+
+for line in (i[1] for i in sorted(d.iteritems(), key = lambda x: x[1]['stats']['participants'])):
+  run = line['stats']
+
+  velocity = run['count'] / run['receive']
+
+  # print run['participants'], run['count'] / run['receive']
+  # inverse
+  # print run['participants'], run['receive'] / run['count']
+
+  # speedup:
+  S_p = run['receive']/run['count'] / 1148.02904
+
+  print run['participants'], velocity, S_p
+

plots/resolution.3D.gmsh.out

@@ -1,3 +1,3 @@
-0.0454730588061  0.000214779089741 7.38934716129e-05 1.41791618738e-05 3.86414745401e-06
+0.0454730588061  0.000270108596188 8.53500404066e-05 1.41791618738e-05 4.9979436523e-06
-0.00997060697355 1.77866400479e-06 1.42892679232e-07 1.01412678674e-08 6.64644538372e-10
+0.00997060697355 2.0540042226e-06 1.69775361716e-07 1.01412678674e-08 8.0860277895e-10
-0.00461278449044 1.76222798245e-07 1.43900476376e-08 3.24157996636e-10 9.90535684124e-12
+0.00461278449044 2.18695319382e-07 1.52441703341e-08 3.24157996636e-10 2.18935788738e-11

plots/scalability_histograms.py

@@ -0,0 +1,35 @@
+import sys
+import shelve
+import pickle
+
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+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()
+
+c = 0
+for line in (i[1] for i in sorted(d.iteritems(), key = lambda x: x[1]['stats']['participants'])):
+  c += 1
+  run = line['stats']
+  x = np.array(run['tasks'].values())
+
+  # the histogram of the data
+  plt.figure(c)
+
+  plt.title("Histogram of Interpolation Performance")
+  plt.xlabel("Number of Interpolations Performed")
+  plt.ylabel("Number of minions")
+
+  n, bins, patches = plt.hist(x) # , 50, normed=1, facecolor='green', alpha=0.5)
+  plt.grid(True)
+  output_file_name = 'asdf.%0.3d.%d.png' % (run['participants'], str(run['count']).count("0"))
+  plt.savefig(output_file_name)

plots/scale.out

@@ -0,0 +1,17 @@
+1 8.71058101972 0.000100000000341
+2 16.9174102062 5.14888564103e-05
+4 33.8342624632 2.57448527477e-05
+5 41.369278595 2.10556754801e-05
+8 70.8631618899 1.22921145728e-05
+11 95.1311787574 9.15638927551e-06
+16 140.277826152 6.20952098301e-06
+22 188.945066816 4.6101129795e-06
+32 275.671657764 3.15976662965e-06
+45 387.071286152 2.25038161213e-06
+64 550.492432027 1.58232530415e-06
+90 774.640026224 1.12446823744e-06
+128 1097.65200754 7.93564899404e-07
+181 1422.36034327 6.12403255659e-07
+196 1500.92599599 5.80347137214e-07
+224 1182.63366931 7.36540931943e-07
+256 1311.56362765 6.64137131117e-07

plots/scale.plt

@@ -0,0 +1,11 @@
+set terminal postscript enhanced
+
+# set nokey
+
+# set log xy
+
+set xlabel "Number of Participating Minions"
+set ylabel "Interpolation Throughput"
+
+plot 'scale.out' u 1:2 w lp t 'velocity'\
+     # ,''          u 1:3 w lp t 'Speedup'