add script for measuring build performance

HACKING

@@ -13,10 +13,12 @@ Testing performance impact of changes:
   If you have a Chrome build handy, it's a good test case.
   Otherwise, https://github.com/martine/ninja/downloads has a copy of
   the Chrome build files (and depfiles). You can untar that, then run
-  "ninja chrome".  I often do something like:
-    (for i in `seq 5`; do time -p ninja chrome) 2>&1 | grep real > old
-    (for i in `seq 5`; do time -p ninja-new chrome) 2>&1 | grep real > new
-  and then compare those two lists of timings either by eye or with R.
+    path/to/my/ninja chrome
+  and compare that against a baseline Ninja.
+
+  There's a script at misc/measure.py that repeatedly runs a command like
+  the above (to address variance) and summarizes its runtime.  E.g.
+    path/to/misc/measure.py path/to/my/ninja chrome
 
   For changing the depfile parser, you can also build 'parser_perftest'
   and run that directly on some representative input files.

misc/measure.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python
+
+# Copyright 2011 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""measure the runtime of a command by repeatedly running it.
+"""
+
+import time
+import subprocess
+import sys
+
+devnull = open('/dev/null', 'w')
+
+def run(cmd, repeat=10):
+    print 'sampling:',
+    sys.stdout.flush()
+
+    samples = []
+    for _ in range(repeat):
+        start = time.time()
+        subprocess.call(cmd, stdout=devnull, stderr=devnull)
+        end = time.time()
+        dt = (end - start) * 1000
+        print '%dms' % int(dt),
+        sys.stdout.flush()
+        samples.append(dt)
+    print
+
+    # We're interested in the 'pure' runtime of the code, which is
+    # conceptually the smallest time we'd see if we ran it enough times
+    # such that it got the perfect time slices / disk cache hits.
+    best = min(samples)
+    # Also print how varied the outputs were in an attempt to make it
+    # more obvious if something has gone terribly wrong.
+    err = sum(s - best for s in samples) / float(len(samples))
+    print 'estimate: %dms (mean err %.1fms)' % (best, err)
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        print 'usage: measure.py command args...'
+        sys.exit(1)
+    run(cmd=sys.argv[1:])