[cig-commits] r19923 - short/3D/PyLith/benchmarks/trunk/static/performance/solvertest

[brad at geodynamics.org]

Author: brad
Date: 2012-04-04 16:38:10 -0700 (Wed, 04 Apr 2012)
New Revision: 19923

Modified:
   short/3D/PyLith/benchmarks/trunk/static/performance/solvertest/plot_scaling.py
Log:
Added iterations.

Modified: short/3D/PyLith/benchmarks/trunk/static/performance/solvertest/plot_scaling.py
===================================================================
--- short/3D/PyLith/benchmarks/trunk/static/performance/solvertest/plot_scaling.py	2012-04-04 22:26:37 UTC (rev 19922)
+++ short/3D/PyLith/benchmarks/trunk/static/performance/solvertest/plot_scaling.py	2012-04-04 23:38:10 UTC (rev 19923)
@@ -18,8 +18,10 @@
 sys.path.append("../../../figures")
 import matplotlibext
 
-nprocs = [1,2,4,8,16,32]
-events = ["Solve",
+header = 0.25 
+
+nprocs = [1,2,4,8,16,32,64]
+stages = ["Solve",
           "Reform Jacobian",
           "Reform Residual",
           ]
@@ -43,39 +45,48 @@
 data = {}
 for c in cells:
     data[c] = {}
-    for e in events:
-        data[c][e] = numpy.zeros(len(nprocs), dtype=numpy.float32)
+    for s in stages:
+        data[c][s] = numpy.zeros(len(nprocs), dtype=numpy.float32)
 
 # Get stats
+niters = {}
 for c in cells:
+    niters[c] = numpy.zeros(len(nprocs), dtype=numpy.float32)
     for ip in xrange(len(nprocs)):
         sys.path.append("logs")
         log = __import__("%s_np%03d" % (c.lower(), nprocs[ip]))
-        for e in events:
+        niters[c][ip] = log.Solve.event['VecMDot'].Count[0]
+        for s in stages:
+            
             total = 0.0
-            eattr = e.replace(' ','_')
-            for sube in log.__getattribute__(eattr).event.values():
-                total += numpy.mean(sube.Time)
-            data[c][e][ip] = total
+            sattr = s.replace(' ','_')
+            assert(nprocs[ip] == log.__getattribute__('Nproc'))
+            data[c][s][ip] = log.__getattribute__(sattr).time / nprocs[ip]
 
-
 figure = matplotlibext.Figure()
-figure.open(3.0, 3.25, margins=[[0.6, 0, 0.1], [0.5, 0, 0.05]], dpi=150)
-ax = figure.axes(1.35, 1, 1.35, 1)
+figure.open(3.0, 5.25, margins=[[0.6, 0, 0.1], [0.5, 0.4, 0.05]], dpi=150)
 
+ax = figure.axes(2.0+header, 1, 1.0+header, 1)
+
 for c in cells:
-    for e in events:
-        ax.loglog(nprocs, data[c][e], 
+    for s in stages:
+        ax.loglog(nprocs, data[c][s], 
                   marker=symdict[c], 
-                  color=styledict[e][0], 
+                  color=styledict[s][0], 
                   linewidth=1,
-                  dashes=styledict[e][1])
+                  dashes=styledict[s][1])
         ax.hold(True)
+        if s == 'Solve':
+            ax.loglog(nprocs, data[c][s]/(niters[c]/niters[c][0]), 
+                      marker=symdict[c], 
+                      color='gray',
+                      linewidth=1,
+                      dashes=styledict[s][1])
 
 ax.set_xlim((1, 128))
-ax.set_xlabel("# Processors", fontsize=10)
+#ax.set_xlabel("# Processors", fontsize=10)
 
-ax.set_ylim((0.01, 500))
+ax.set_ylim((0.01, 200))
 ax.set_ylabel("Time (s)", fontsize=10)
 
 import matplotlib.lines as lines
@@ -89,14 +100,30 @@
           loc='lower left', bbox_to_anchor=(0,1.05), borderaxespad=0)
 
 proxies = []
-for e in events:
+for s in stages:
     proxies.append(lines.Line2D((0,0),(1,1), 
                                 marker=None, 
-                                color=styledict[e][0],
-                                dashes=styledict[e][1]))
-ax.legend(proxies, events, 
+                                color=styledict[s][0],
+                                dashes=styledict[s][1]))
+ax.legend(proxies, stages, 
           loc='lower right', bbox_to_anchor=(1,1.05), borderaxespad=0)
 ax.add_artist(l1)
 
+ax = figure.axes(2.0+header, 1, 2.0+header, 1)
+
+for c in cells:
+    ax.semilogx(nprocs, niters[c], 
+            marker=symdict[c], 
+            color='red', 
+            linewidth=1)
+    ax.hold(True)
+
+ax.set_xlim((1, 128))
+ax.set_xlabel("# Processors", fontsize=10)
+
+ax.set_ylim((0, 150))
+ax.set_ylabel("# Iterations", fontsize=10)
+
+
 pyplot.show()
 pyplot.savefig('scaling.pdf')