1 files changed, 141 insertions, 0 deletions
diff --git a/benchtests/scripts/import_bench.py b/benchtests/scripts/import_bench.py
new file mode 100644
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+++ b/benchtests/scripts/import_bench.py
@@ -0,0 +1,141 @@
+#!/usr/bin/python
+# Copyright (C) 2015 Free Software Foundation, Inc.
+# This file is part of the GNU C Library.
+#
+# The GNU C Library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License, or (at your option) any later version.
+#
+# The GNU C Library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with the GNU C Library; if not, see
+# <http://www.gnu.org/licenses/>.
+"""Functions to import benchmark data and process it"""
+
+import json
+try:
+    import jsonschema as validator
+except ImportError:
+    print('Could not find jsonschema module.')
+    raise
+
+
+def mean(lst):
+    """Compute and return mean of numbers in a list
+
+    The numpy average function has horrible performance, so implement our
+    own mean function.
+
+    Args:
+        lst: The list of numbers to average.
+    Return:
+        The mean of members in the list.
+    """
+    return sum(lst) / len(lst)
+
+
+def split_list(bench, func, var):
+    """ Split the list into a smaller set of more distinct points
+
+    Group together points such that the difference between the smallest
+    point and the mean is less than 1/3rd of the mean.  This means that
+    the mean is at most 1.5x the smallest member of that group.
+
+    mean - xmin < mean / 3
+    i.e. 2 * mean / 3 < xmin
+    i.e. mean < 3 * xmin / 2
+
+    For an evenly distributed group, the largest member will be less than
+    twice the smallest member of the group.
+    Derivation:
+
+    An evenly distributed series would be xmin, xmin + d, xmin + 2d...
+
+    mean = (2 * n * xmin + n * (n - 1) * d) / 2 * n
+    and max element is xmin + (n - 1) * d
+
+    Now, mean < 3 * xmin / 2
+
+    3 * xmin > 2 * mean
+    3 * xmin > (2 * n * xmin + n * (n - 1) * d) / n
+    3 * n * xmin > 2 * n * xmin + n * (n - 1) * d
+    n * xmin > n * (n - 1) * d
+    xmin > (n - 1) * d
+    2 * xmin > xmin + (n-1) * d
+    2 * xmin > xmax
+
+    Hence, proved.
+
+    Similarly, it is trivial to prove that for a similar aggregation by using
+    the maximum element, the maximum element in the group must be at most 4/3
+    times the mean.
+
+    Args:
+        bench: The benchmark object
+        func: The function name
+        var: The function variant name
+    """
+    means = []
+    lst = bench['functions'][func][var]['timings']
+    last = len(lst) - 1
+    while lst:
+        for i in range(last + 1):
+            avg = mean(lst[i:])
+            if avg > 0.75 * lst[last]:
+                means.insert(0, avg)
+                lst = lst[:i]
+                last = i - 1
+                break
+    bench['functions'][func][var]['timings'] = means
+
+
+def do_for_all_timings(bench, callback):
+    """Call a function for all timing objects for each function and its
+    variants.
+
+    Args:
+        bench: The benchmark object
+        callback: The callback function
+    """
+    for func in bench['functions'].keys():
+        for k in bench['functions'][func].keys():
+            if 'timings' not in bench['functions'][func][k].keys():
+                continue
+
+            callback(bench, func, k)
+
+
+def compress_timings(points):
+    """Club points with close enough values into a single mean value
+
+    See split_list for details on how the clubbing is done.
+
+    Args:
+        points: The set of points.
+    """
+    do_for_all_timings(points, split_list)
+
+
+def parse_bench(filename, schema_filename):
+    """Parse the input file
+
+    Parse and validate the json file containing the benchmark outputs.  Return
+    the resulting object.
+    Args:
+        filename: Name of the benchmark output file.
+    Return:
+        The bench dictionary.
+    """
+    with open(schema_filename, 'r') as schemafile:
+        schema = json.load(schemafile)
+        with open(filename, 'r') as benchfile:
+            bench = json.load(benchfile)
+            validator.validate(bench, schema)
+            do_for_all_timings(bench, lambda b, f, v:
+                    b['functions'][f][v]['timings'].sort())
+            return bench