Dump profile-based number of iterations

	* analyze_brprob.py: Parse and display average number
	of loop iterations.
	* cfgloop.c (flow_loop_dump): Dump average number of loop iterations.
	* cfgloop.h: Change 'struct loop' to 'const struct loop' for a
	few functions.
	* cfgloopanal.c (expected_loop_iterations_unbounded): Set a new
	argument to true if the expected number of iterations is
	loop-based.

From-SVN: r237762

1 files changed, 33 insertions, 0 deletions

diff --git a/contrib/analyze_brprob.py b/contrib/analyze_brprob.py
index 2526623..c276d81 100755
--- a/contrib/analyze_brprob.py
+++ b/contrib/analyze_brprob.py
@@ -67,9 +67,26 @@ import os
 import re
 import argparse
 
+from math import *
+
 def percentage(a, b):
     return 100.0 * a / b
 
+def average(values):
+    return 1.0 * sum(values) / len(values)
+
+def average_cutoff(values, cut):
+    l = len(values)
+    skip = floor(l * cut / 2)
+    if skip > 0:
+        values.sort()
+        values = values[skip:-skip]
+    return average(values)
+
+def median(values):
+    values.sort()
+    return values[int(len(values) / 2)]
+
 class Summary:
     def __init__(self, name):
         self.name = name
@@ -93,6 +110,7 @@ class Profile:
     def __init__(self, filename):
         self.filename = filename
         self.heuristics = {}
+        self.niter_vector = []
 
     def add(self, name, prediction, count, hits):
         if not name in self.heuristics:
@@ -106,6 +124,10 @@ class Profile:
         s.hits += hits
         s.fits += max(hits, count - hits)
 
+    def add_loop_niter(self, niter):
+        if niter > 0:
+            self.niter_vector.append(niter)
+
     def branches_max(self):
         return max([v.branches for k, v in self.heuristics.items()])
 
@@ -127,6 +149,13 @@ class Profile:
              percentage(v.hits, v.count), percentage(v.fits, v.count),
              v.count, v.count_formatted(), percentage(v.count, self.count_max()) ))
 
+        print ('\nLoop count: %d' % len(self.niter_vector)),
+        print('  avg. # of iter: %.2f' % average(self.niter_vector))
+        print('  median # of iter: %.2f' % median(self.niter_vector))
+        for v in [1, 5, 10, 20, 30]:
+            cut = 0.01 * v
+            print('  avg. (%d%% cutoff) # of iter: %.2f' % (v, average_cutoff(self.niter_vector, cut)))
+
 parser = argparse.ArgumentParser()
 parser.add_argument('dump_file', metavar = 'dump_file', help = 'IPA profile dump file')
 parser.add_argument('-s', '--sorting', dest = 'sorting', choices = ['branches', 'hitrate', 'coverage'], default = 'branches')
@@ -135,6 +164,7 @@ args = parser.parse_args()
 
 profile = Profile(sys.argv[1])
 r = re.compile('  (.*) heuristics( of edge [0-9]*->[0-9]*)?( \\(.*\\))?: (.*)%.*exec ([0-9]*) hit ([0-9]*)')
+loop_niter_str = ';;  profile-based iteration count: '
 for l in open(args.dump_file).readlines():
     m = r.match(l)
     if m != None and m.group(3) == None:
@@ -144,5 +174,8 @@ for l in open(args.dump_file).readlines():
         hits = int(m.group(6))
 
         profile.add(name, prediction, count, hits)
+    elif l.startswith(loop_niter_str):
+        v = int(l[len(loop_niter_str):])
+        profile.add_loop_niter(v)
 
 profile.dump(args.sorting)