6ff9439afe
are ours
arch/alpha/alpha_linux_process.hh:
arch/alpha/alpha_tru64_process.hh:
base/loader/object_file.cc:
base/loader/object_file.hh:
sim/process.cc:
sim/process.hh:
remove $Id$ string
cpu/ozone/cpu.cc:
cpu/ozone/cpu_impl.hh:
cpu/ozone/ea_list.cc:
cpu/ozone/ea_list.hh:
kern/linux/sched.hh:
kern/linux/thread_info.hh:
Add M5 Copyright
cpu/trace/opt_cpu.cc:
dev/rtcreg.h:
nit
kern/linux/aligned.hh:
kern/linux/hwrpb.hh:
util/oprofile-top.py:
util/stats/db.py:
util/stats/dbinit.py:
util/stats/display.py:
util/stats/info.py:
util/stats/print.py:
util/stats/stats.py:
Cleanup copyright
--HG--
extra : convert_revision : 4274e9121ef7543e0b3999b31e935edb19c54d46
768 lines
22 KiB
Python
768 lines
22 KiB
Python
# Copyright (c) 2003-2004 The Regents of The University of Michigan
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met: redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer;
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# redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution;
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# neither the name of the copyright holders nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from __future__ import division
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import operator, re, types
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source = None
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display_run = 0
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global globalTicks
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globalTicks = None
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def total(f):
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if isinstance(f, FormulaStat):
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v = f.value
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else:
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v = f
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f = FormulaStat()
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if isinstance(v, (list, tuple)):
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f.value = reduce(operator.add, v)
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else:
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f.value = v
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return f
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def unaryop(op, f):
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if isinstance(f, FormulaStat):
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v = f.value
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else:
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v = f
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if isinstance(v, (list, tuple)):
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return map(op, v)
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else:
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return op(v)
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def zerodiv(lv, rv):
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if rv == 0.0:
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return 0.0
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else:
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return operator.truediv(lv, rv)
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def wrapop(op, lv, rv):
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if isinstance(lv, str):
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return lv
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if isinstance(rv, str):
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return rv
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return op(lv, rv)
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def same(lrun, rrun):
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for lx,rx in zip(lrun.keys(),rrun.keys()):
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if lx != rx:
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print 'lx != rx'
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print lx, rx
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print lrun.keys()
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print rrun.keys()
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return False
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for ly,ry in zip(lrun[lx].keys(),rrun[rx].keys()):
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if ly != ry:
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print 'ly != ry'
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print ly, ry
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print lrun[lx].keys()
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print rrun[rx].keys()
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return False
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return True
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def binaryop(op, lf, rf):
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result = {}
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if isinstance(lf, FormulaStat) and isinstance(rf, FormulaStat):
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lv = lf.value
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rv = rf.value
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theruns = []
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for r in lv.keys():
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if rv.has_key(r):
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if same(lv[r], rv[r]):
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theruns.append(r)
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else:
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raise AttributeError
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for run in theruns:
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result[run] = {}
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for x in lv[run].keys():
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result[run][x] = {}
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for y in lv[run][x].keys():
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result[run][x][y] = wrapop(op, lv[run][x][y],
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rv[run][x][y])
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elif isinstance(lf, FormulaStat):
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lv = lf.value
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for run in lv.keys():
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result[run] = {}
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for x in lv[run].keys():
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result[run][x] = {}
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for y in lv[run][x].keys():
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result[run][x][y] = wrapop(op, lv[run][x][y], rf)
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elif isinstance(rf, FormulaStat):
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rv = rf.value
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for run in rv.keys():
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result[run] = {}
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for x in rv[run].keys():
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result[run][x] = {}
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for y in rv[run][x].keys():
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result[run][x][y] = wrapop(op, lf, rv[run][x][y])
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return result
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def sums(x, y):
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if isinstance(x, (list, tuple)):
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return map(lambda x, y: x + y, x, y)
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else:
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return x + y
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def alltrue(seq):
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return reduce(lambda x, y: x and y, seq)
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def allfalse(seq):
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return not reduce(lambda x, y: x or y, seq)
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def enumerate(seq):
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return map(None, range(len(seq)), seq)
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def cmp(a, b):
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if a < b:
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return -1
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elif a == b:
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return 0
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else:
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return 1
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class Statistic(object):
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def __init__(self, data):
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self.__dict__.update(data.__dict__)
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if not self.__dict__.has_key('value'):
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self.__dict__['value'] = None
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if not self.__dict__.has_key('bins'):
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self.__dict__['bins'] = None
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if not self.__dict__.has_key('ticks'):
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self.__dict__['ticks'] = None
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if 'vc' not in self.__dict__:
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self.vc = {}
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def __getattribute__(self, attr):
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if attr == 'ticks':
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if self.__dict__['ticks'] != globalTicks:
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self.__dict__['value'] = None
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self.__dict__['ticks'] = globalTicks
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return self.__dict__['ticks']
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if attr == 'value':
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if self.__dict__['ticks'] != globalTicks:
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if self.__dict__['ticks'] != None and \
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len(self.__dict__['ticks']) == 1:
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self.vc[self.__dict__['ticks'][0]] = self.__dict__['value']
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self.__dict__['ticks'] = globalTicks
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if len(globalTicks) == 1 and self.vc.has_key(globalTicks[0]):
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self.__dict__['value'] = self.vc[globalTicks[0]]
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else:
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self.__dict__['value'] = None
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if self.__dict__['value'] == None:
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self.__dict__['value'] = self.getValue()
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return self.__dict__['value']
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else:
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return super(Statistic, self).__getattribute__(attr)
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def __setattr__(self, attr, value):
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if attr == 'bins' or attr == 'ticks':
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if attr == 'bins':
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if value is not None:
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value = source.getBin(value)
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#elif attr == 'ticks' and type(value) is str:
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# value = [ int(x) for x in value.split() ]
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self.__dict__[attr] = value
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self.__dict__['value'] = None
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self.vc = {}
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else:
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super(Statistic, self).__setattr__(attr, value)
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def getValue(self):
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raise AttributeError, 'getValue() must be defined'
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def zero(self):
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return False
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def __ne__(self, other):
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return not (self == other)
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def __str__(self):
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return '%f' % (float(self))
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class FormulaStat(object):
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def __add__(self, other):
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f = FormulaStat()
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f.value = binaryop(operator.add, self, other)
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return f
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def __sub__(self, other):
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f = FormulaStat()
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f.value = binaryop(operator.sub, self, other)
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return f
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def __mul__(self, other):
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f = FormulaStat()
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f.value = binaryop(operator.mul, self, other)
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return f
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def __truediv__(self, other):
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f = FormulaStat()
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f.value = binaryop(zerodiv, self, other)
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return f
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def __mod__(self, other):
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f = FormulaStat()
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f.value = binaryop(operator.mod, self, other)
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return f
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def __radd__(self, other):
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f = FormulaStat()
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f.value = binaryop(operator.add, other, self)
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return f
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def __rsub__(self, other):
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f = FormulaStat()
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f.value = binaryop(operator.sub, other, self)
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return f
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def __rmul__(self, other):
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f = FormulaStat()
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f.value = binaryop(operator.mul, other, self)
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return f
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def __rtruediv__(self, other):
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f = FormulaStat()
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f.value = binaryop(zerodiv, other, self)
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return f
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def __rmod__(self, other):
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f = FormulaStat()
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f.value = binaryop(operator.mod, other, self)
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return f
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def __neg__(self):
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f = FormulaStat()
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f.value = unaryop(operator.neg, self)
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return f
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def __getitem__(self, idx):
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f = FormulaStat()
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f.value = {}
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for key in self.value.keys():
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f.value[key] = {}
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f.value[key][0] = {}
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f.value[key][0][0] = self.value[key][idx][0]
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return f
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def __float__(self):
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if isinstance(self.value, FormulaStat):
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return float(self.value)
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if not self.value.has_key(display_run):
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return (1e300*1e300)
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if len(self.value[display_run]) == 1:
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return self.value[display_run][0][0]
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else:
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#print self.value[display_run]
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return self.value[display_run][4][0]
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#raise ValueError
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def display(self):
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import display
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d = display.VectorDisplay()
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d.flags = 0
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d.precision = 1
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d.name = 'formula'
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d.desc = 'formula'
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val = self.value[display_run]
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d.value = [ val[x][0] for x in val.keys() ]
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d.display()
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class Scalar(Statistic,FormulaStat):
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def getValue(self):
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return source.data(self, self.bins, self.ticks)
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def display(self):
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import display
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p = display.Print()
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p.name = self.name
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p.desc = self.desc
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p.value = float(self)
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p.flags = self.flags
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p.precision = self.precision
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if display.all or (self.flags & flags.printable):
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p.display()
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def comparable(self, other):
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return self.name == other.name
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def __eq__(self, other):
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return self.value == other.value
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def __isub__(self, other):
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self.value -= other.value
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return self
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def __iadd__(self, other):
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self.value += other.value
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return self
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def __itruediv__(self, other):
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if not other:
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return self
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self.value /= other
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return self
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class Vector(Statistic,FormulaStat):
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def getValue(self):
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return source.data(self, self.bins, self.ticks);
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def display(self):
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import display
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if not display.all and not (self.flags & flags.printable):
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return
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d = display.VectorDisplay()
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d.__dict__.update(self.__dict__)
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d.display()
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def comparable(self, other):
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return self.name == other.name and \
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len(self.value) == len(other.value)
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def __eq__(self, other):
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if isinstance(self.value, (list, tuple)) != \
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isinstance(other.value, (list, tuple)):
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return False
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if isinstance(self.value, (list, tuple)):
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if len(self.value) != len(other.value):
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return False
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else:
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for v1,v2 in zip(self.value, other.value):
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if v1 != v2:
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return False
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return True
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else:
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return self.value == other.value
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def __isub__(self, other):
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self.value = binaryop(operator.sub, self.value, other.value)
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return self
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def __iadd__(self, other):
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self.value = binaryop(operator.add, self.value, other.value)
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return self
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def __itruediv__(self, other):
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if not other:
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return self
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if isinstance(self.value, (list, tuple)):
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for i in xrange(len(self.value)):
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self.value[i] /= other
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else:
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self.value /= other
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return self
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class Formula(Vector):
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def getValue(self):
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formula = re.sub(':', '__', self.formula)
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x = eval(formula, source.stattop)
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return x.value
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def comparable(self, other):
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return self.name == other.name and \
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compare(self.dist, other.dist)
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def __eq__(self, other):
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return self.value == other.value
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def __isub__(self, other):
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return self
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def __iadd__(self, other):
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return self
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def __itruediv__(self, other):
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if not other:
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return self
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return self
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class SimpleDist(object):
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def __init__(self, sums, squares, samples):
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self.sums = sums
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self.squares = squares
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self.samples = samples
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def getValue(self):
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return 0.0
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def display(self, name, desc, flags, precision):
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import display
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p = display.Print()
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p.flags = flags
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p.precision = precision
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if self.samples > 0:
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p.name = name + ".mean"
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p.value = self.sums / self.samples
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p.display()
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p.name = name + ".stdev"
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if self.samples > 1:
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var = (self.samples * self.squares - self.sums ** 2) \
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/ (self.samples * (self.samples - 1))
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if var >= 0:
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p.value = math.sqrt(var)
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else:
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p.value = 'NaN'
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else:
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p.value = 0.0
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p.display()
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p.name = name + ".samples"
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p.value = self.samples
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p.display()
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def comparable(self, other):
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return True
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def __eq__(self, other):
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return self.sums == other.sums and self.squares == other.squares and \
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self.samples == other.samples
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def __isub__(self, other):
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self.sums -= other.sums
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self.squares -= other.squares
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self.samples -= other.samples
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return self
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def __iadd__(self, other):
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self.sums += other.sums
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self.squares += other.squares
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self.samples += other.samples
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return self
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def __itruediv__(self, other):
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if not other:
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return self
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self.sums /= other
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self.squares /= other
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self.samples /= other
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return self
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class FullDist(SimpleDist):
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def __init__(self, sums, squares, samples, minval, maxval,
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under, vec, over, min, max, bsize, size):
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self.sums = sums
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self.squares = squares
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self.samples = samples
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self.minval = minval
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self.maxval = maxval
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self.under = under
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self.vec = vec
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self.over = over
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self.min = min
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self.max = max
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self.bsize = bsize
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self.size = size
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def getValue(self):
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return 0.0
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def display(self, name, desc, flags, precision):
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import display
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p = display.Print()
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p.flags = flags
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p.precision = precision
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p.name = name + '.min_val'
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p.value = self.minval
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p.display()
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p.name = name + '.max_val'
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p.value = self.maxval
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p.display()
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p.name = name + '.underflow'
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p.value = self.under
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p.display()
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i = self.min
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for val in self.vec[:-1]:
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p.name = name + '[%d:%d]' % (i, i + self.bsize - 1)
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p.value = val
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p.display()
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i += self.bsize
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p.name = name + '[%d:%d]' % (i, self.max)
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p.value = self.vec[-1]
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p.display()
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p.name = name + '.overflow'
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p.value = self.over
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p.display()
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SimpleDist.display(self, name, desc, flags, precision)
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def comparable(self, other):
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return self.min == other.min and self.max == other.max and \
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self.bsize == other.bsize and self.size == other.size
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def __eq__(self, other):
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return self.sums == other.sums and self.squares == other.squares and \
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self.samples == other.samples
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def __isub__(self, other):
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self.sums -= other.sums
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self.squares -= other.squares
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self.samples -= other.samples
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if other.samples:
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self.minval = min(self.minval, other.minval)
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self.maxval = max(self.maxval, other.maxval)
|
|
self.under -= under
|
|
self.vec = map(lambda x,y: x - y, self.vec, other.vec)
|
|
self.over -= over
|
|
return self
|
|
|
|
def __iadd__(self, other):
|
|
if not self.samples and other.samples:
|
|
self = other
|
|
return self
|
|
|
|
self.sums += other.sums
|
|
self.squares += other.squares
|
|
self.samples += other.samples
|
|
|
|
if other.samples:
|
|
self.minval = min(self.minval, other.minval)
|
|
self.maxval = max(self.maxval, other.maxval)
|
|
self.under += other.under
|
|
self.vec = map(lambda x,y: x + y, self.vec, other.vec)
|
|
self.over += other.over
|
|
return self
|
|
|
|
def __itruediv__(self, other):
|
|
if not other:
|
|
return self
|
|
self.sums /= other
|
|
self.squares /= other
|
|
self.samples /= other
|
|
|
|
if self.samples:
|
|
self.under /= other
|
|
for i in xrange(len(self.vec)):
|
|
self.vec[i] /= other
|
|
self.over /= other
|
|
return self
|
|
|
|
class Dist(Statistic):
|
|
def getValue(self):
|
|
return 0.0
|
|
|
|
def display(self):
|
|
import display
|
|
if not display.all and not (self.flags & flags.printable):
|
|
return
|
|
|
|
self.dist.display(self.name, self.desc, self.flags, self.precision)
|
|
|
|
def comparable(self, other):
|
|
return self.name == other.name and \
|
|
self.dist.compareable(other.dist)
|
|
|
|
def __eq__(self, other):
|
|
return self.dist == other.dist
|
|
|
|
def __isub__(self, other):
|
|
self.dist -= other.dist
|
|
return self
|
|
|
|
def __iadd__(self, other):
|
|
self.dist += other.dist
|
|
return self
|
|
|
|
def __itruediv__(self, other):
|
|
if not other:
|
|
return self
|
|
self.dist /= other
|
|
return self
|
|
|
|
class VectorDist(Statistic):
|
|
def getValue(self):
|
|
return 0.0
|
|
|
|
def display(self):
|
|
import display
|
|
if not display.all and not (self.flags & flags.printable):
|
|
return
|
|
|
|
if isinstance(self.dist, SimpleDist):
|
|
return
|
|
|
|
for dist,sn,sd,i in map(None, self.dist, self.subnames, self.subdescs,
|
|
range(len(self.dist))):
|
|
if len(sn) > 0:
|
|
name = '%s.%s' % (self.name, sn)
|
|
else:
|
|
name = '%s[%d]' % (self.name, i)
|
|
|
|
if len(sd) > 0:
|
|
desc = sd
|
|
else:
|
|
desc = self.desc
|
|
|
|
dist.display(name, desc, self.flags, self.precision)
|
|
|
|
if (self.flags & flags.total) or 1:
|
|
if isinstance(self.dist[0], SimpleDist):
|
|
disttotal = SimpleDist( \
|
|
reduce(sums, [d.sums for d in self.dist]),
|
|
reduce(sums, [d.squares for d in self.dist]),
|
|
reduce(sums, [d.samples for d in self.dist]))
|
|
else:
|
|
disttotal = FullDist( \
|
|
reduce(sums, [d.sums for d in self.dist]),
|
|
reduce(sums, [d.squares for d in self.dist]),
|
|
reduce(sums, [d.samples for d in self.dist]),
|
|
min([d.minval for d in self.dist]),
|
|
max([d.maxval for d in self.dist]),
|
|
reduce(sums, [d.under for d in self.dist]),
|
|
reduce(sums, [d.vec for d in self.dist]),
|
|
reduce(sums, [d.over for d in self.dist]),
|
|
dist[0].min,
|
|
dist[0].max,
|
|
dist[0].bsize,
|
|
dist[0].size)
|
|
|
|
name = '%s.total' % (self.name)
|
|
desc = self.desc
|
|
disttotal.display(name, desc, self.flags, self.precision)
|
|
|
|
def comparable(self, other):
|
|
return self.name == other.name and \
|
|
alltrue(map(lambda x, y : x.comparable(y),
|
|
self.dist,
|
|
other.dist))
|
|
|
|
def __eq__(self, other):
|
|
return alltrue(map(lambda x, y : x == y, self.dist, other.dist))
|
|
|
|
def __isub__(self, other):
|
|
if isinstance(self.dist, (list, tuple)) and \
|
|
isinstance(other.dist, (list, tuple)):
|
|
for sd,od in zip(self.dist, other.dist):
|
|
sd -= od
|
|
else:
|
|
self.dist -= other.dist
|
|
return self
|
|
|
|
def __iadd__(self, other):
|
|
if isinstance(self.dist, (list, tuple)) and \
|
|
isinstance(other.dist, (list, tuple)):
|
|
for sd,od in zip(self.dist, other.dist):
|
|
sd += od
|
|
else:
|
|
self.dist += other.dist
|
|
return self
|
|
|
|
def __itruediv__(self, other):
|
|
if not other:
|
|
return self
|
|
if isinstance(self.dist, (list, tuple)):
|
|
for dist in self.dist:
|
|
dist /= other
|
|
else:
|
|
self.dist /= other
|
|
return self
|
|
|
|
class Vector2d(Statistic):
|
|
def getValue(self):
|
|
return 0.0
|
|
|
|
def display(self):
|
|
import display
|
|
if not display.all and not (self.flags & flags.printable):
|
|
return
|
|
|
|
d = display.VectorDisplay()
|
|
d.__dict__.update(self.__dict__)
|
|
|
|
if self.__dict__.has_key('ysubnames'):
|
|
ysubnames = list(self.ysubnames)
|
|
slack = self.x - len(ysubnames)
|
|
if slack > 0:
|
|
ysubnames.extend(['']*slack)
|
|
else:
|
|
ysubnames = range(self.x)
|
|
|
|
for x,sname in enumerate(ysubnames):
|
|
o = x * self.y
|
|
d.value = self.value[o:o+self.y]
|
|
d.name = '%s[%s]' % (self.name, sname)
|
|
d.display()
|
|
|
|
if self.flags & flags.total:
|
|
d.value = []
|
|
for y in range(self.y):
|
|
xtot = 0.0
|
|
for x in range(self.x):
|
|
xtot += self.value[y + x * self.x]
|
|
d.value.append(xtot)
|
|
|
|
d.name = self.name + '.total'
|
|
d.display()
|
|
|
|
def comparable(self, other):
|
|
return self.name == other.name and self.x == other.x and \
|
|
self.y == other.y
|
|
|
|
def __eq__(self, other):
|
|
return True
|
|
|
|
def __isub__(self, other):
|
|
return self
|
|
|
|
def __iadd__(self, other):
|
|
return self
|
|
|
|
def __itruediv__(self, other):
|
|
if not other:
|
|
return self
|
|
return self
|
|
|
|
def NewStat(data):
|
|
stat = None
|
|
if data.type == 'SCALAR':
|
|
stat = Scalar(data)
|
|
elif data.type == 'VECTOR':
|
|
stat = Vector(data)
|
|
elif data.type == 'DIST':
|
|
stat = Dist(data)
|
|
elif data.type == 'VECTORDIST':
|
|
stat = VectorDist(data)
|
|
elif data.type == 'VECTOR2D':
|
|
stat = Vector2d(data)
|
|
elif data.type == 'FORMULA':
|
|
stat = Formula(data)
|
|
|
|
return stat
|
|
|