Generate latency graphs and integrate them into the page.

web/processing/gen-details-page.py

@@ -62,17 +62,23 @@
 os_string = "unknown"
 for line in open(data_dir + "/logs/uname").readlines():
   fields = line.split()
-  os = fields[0]
+  os_id = fields[0]
   ver = fields[1]
   arch = fields[2]
-  os_string = "%s %s, %s" % (os, ver, arch)
+  os_string = "%s %s, %s" % (os_id, ver, arch)
 
 # Generate latency graphs
-l = os.listdir(results_dir + "/lat")
-for lat_file in l:
-  print lat_file
-  argv = ["python", "plot_lat.py", results_dir + "/lat/" + lat_file, lat_file]
-  subprocess.check_call(argv)
+lat_ls = None
+try:
+  lat_ls = os.listdir(results_dir + "/lat")
+  for lat_file in lat_ls:
+    if not re.search("\.csv", lat_file):
+      continue
+    argv = ["python", "plot_lat.py", results_dir + "/lat/" + lat_file, \
+            lat_file, data_dir + "/graphs", str(len(processor_ids))]
+    subprocess.check_call(argv)
+except:
+  pass
 
 # Generate throughput graphs
 argv = ["python", "plot_thr.py", results_dir, target_cpus, "0"]
@@ -98,8 +104,26 @@
 out_html = out_html + html
 
 # Generate latency heatmap section
-html = "<h2>Latency</h2><p>To be added.</p>"
+html = "<h2>Latency</h2>"
+html = html + "<p>These graphs show the pairwise IPC latency between cores."
+html = html + "<table><tr>"
+thr_graphs_links = ""
+i = 0
+graphs_per_row = 3
+if lat_ls is None:
+  html = html + "<td><b><em>No data.</em></b></td>"
+else:
+  for t in lat_ls:
+    if not re.search("\.csv", t):
+      continue
+    graph_file = "../graphs/%s/lat_%s.png" % (name, t)
+    if i % graphs_per_row == 0:
+      html = html + "</tr><tr>"
+    html = html + "<td><a href=\"%s\"><img src=\"%s\" /></a></td>" \
+        % (graph_file, graph_file)
+    i = i + 1
 
+html = html + "</tr></table>"
 out_html = out_html + html
 
 # Generate throughput graphs section

web/processing/plot_lat.py

@@ -6,7 +6,7 @@
 import numpy.random
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
-import pylab
+import pylab as pyl
 
 def get_data(filename):
   data = np.loadtxt(filename)
@@ -30,19 +30,24 @@ def get_data(filename):
 # Handle command line args
 
 if len(sys.argv) < 2:
-  print "usage: python plot_lat.py <input file> <title> [fix-scale]"
+  print "usage: python plot_lat.py <input file> <title> <output_dir> <num_cores> [fix-scale]"
   sys.exit(0)
 
 input_file = sys.argv[1]
 
 fix_scale = 0
-if len(sys.argv) > 3:
-  fix_scale = int(sys.argv[3])
+if len(sys.argv) > 5:
+  fix_scale = int(sys.argv[5])
+
+num_cores = int(sys.argv[4])
+
+output_dir = sys.argv[3]
 
 raw_data = np.loadtxt(input_file)
 data = get_data(input_file)
 
-fig = plt.figure(figsize=(4,3))
+fig = plt.figure(figsize=(3,2))
+pyl.rc('font', size='8.0')
 #f = pylab.Figure(figsize=(2,1.5))
 #  print ds
 #heatmap, xedges, yedges = np.histogram2d(data[0], data[1], bins=48, weights=data[2])
@@ -61,13 +66,13 @@ def get_data(filename):
 
 # add some
 plt.ylabel('Core ID')
-plt.ylim(0, 48)
+plt.ylim(-0.5, int(sys.argv[4])-0.5)
 plt.xlabel('Core ID')
-plt.xlim(0, 48)
+plt.xlim(-0.5, int(sys.argv[4])-0.5)
 plt.title(sys.argv[2])
 
 cb = plt.colorbar(shrink=1.0, format='%.3e')
 cb.set_label('Latency in microseconds')
 
 #plt.savefig("lat_" + sys.argv[1] + ".pdf", format="pdf", bbox_inches='tight')
-plt.savefig(out_dir + "/lat_" + sys.argv[1] + ".png", format="png", bbox_inches='tight')
+plt.savefig(output_dir + "/lat_" + sys.argv[2] + ".png", format="png", bbox_inches='tight')