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#!/usr/bin/env python
# Copyright (C) 2009 by Aaron Ariel
# and the University of British Columbia, Vancouver,
# BC V6T 1Z4, All Rights Reserved.
#
# THIS IS A LEGAL DOCUMENT BY DOWNLOADING GPGPU-SIM, YOU ARE AGREEING TO THESE
# TERMS AND CONDITIONS.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNERS OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
# NOTE: The files libcuda/cuda_runtime_api.c and src/cuda-sim/cuda-math.h
# are derived from the CUDA Toolset available from http://www.nvidia.com/cuda
# (property of NVIDIA). The files benchmarks/BlackScholes/ and
# benchmarks/template/ are derived from the CUDA SDK available from
# http://www.nvidia.com/cuda (also property of NVIDIA). The files from
# src/intersim/ are derived from Booksim (a simulator provided with the
# textbook "Principles and Practices of Interconnection Networks" available
# from http://cva.stanford.edu/books/ppin/). As such, those files are bound by
# the corresponding legal terms and conditions set forth separately (original
# copyright notices are left in files from these sources and where we have
# modified a file our copyright notice appears before the original copyright
# notice).
#
# Using this version of GPGPU-Sim requires a complete installation of CUDA
# which is distributed seperately by NVIDIA under separate terms and
# conditions. To use this version of GPGPU-Sim with OpenCL requires a
# recent version of NVIDIA's drivers which support OpenCL.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the University of British Columbia nor the names of
# its contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# 4. This version of GPGPU-SIM is distributed freely for non-commercial use only.
#
# 5. No nonprofit user may place any restrictions on the use of this software,
# including as modified by the user, by any other authorized user.
#
# 6. GPGPU-SIM was developed primarily by Tor M. Aamodt, Wilson W. L. Fung,
# Ali Bakhoda, George L. Yuan, at the University of British Columbia,
# Vancouver, BC V6T 1Z4
class variable:
# normal constructor used by internal types
def __init__(self, lookup_tag, type, bool, organize = 'custom', datatype = int):
self.data = []
self.lookup_tag = lookup_tag # the stat name in the log file (can be different from the GUI)
self.type = type # plot type
self.bool = bool # wheither to expect reset at the end of a kernel
self.organize = organize # how is the data organize in the log, see organizedata.py for options
self.datatype = datatype # int or float or other custom type?
# import the stat variable setting from a string in variables.txt or a custom header
def importFromString(self, string_spec):
data_type_str = {'int':int, 'float':float}
plot_type_str = {'scalar': 1, 'vector': 2, 'stackedbar': 3, 'vector2d': 4}
organize_str = {'scalar': 'scalar', 'implicit': 'impVec', 'index': 'idxVec', 'index2d': 'idx2DVec'} #skip custom
try:
# initialize new stat variable with info from input string
self.data = []
self.lookup_tag = ''
spec = [token.strip().lower() for token in string_spec.split(",")]
self.lookup_tag = spec[0]
self.type = plot_type_str[spec[1]]
self.bool = int(spec[2])
self.organize = organize_str[spec[3]]
self.datatype = data_type_str[spec[4]]
# guard against bogus entries
if (self.type == 1):
assert(self.organize == 'scalar')
elif (self.type == 2):
assert(self.organize in ['impVec', 'idxVec'])
elif (self.type == 3):
assert(self.organize in ['impVec', 'idxVec'])
elif (self.type == 4):
assert(self.organize == 'idx2DVec')
except Exception, (e):
print "Error in creating new stat variable from string: %s" % string_spec
raise e
class bookmark:
def __init__(self):
self.title = ""
self.fileChosen = []
self.dataChosenX = []
self.dataChosenY = []
self.graphChosen = []
self.dydx = []
self.description = ""
global lineStatName
lineStatName = ['count', 'latency', 'dram_traffic', 'smem_bk_conflicts', 'smem_warp',
'gmem_access_generated', 'gmem_warp', 'exposed_latency', 'warp_divergence',
'warp_issued']
def loadLineStatName(filename):
global lineStatName
file = open(filename, 'r')
while file:
line = file.readline()
if not line : break
if (line.startswith('kernel line :')) :
line = line.strip()
ptxLineStatName = line.split(' ')
ptxLineStatName = ptxLineStatName[3:]
lineStatName = ptxLineStatName
break
class cudaLineNo:
debug = 0
def __init__(self, ptxLines, ptxStats):
self.stats = {}
self.ptxLines = ptxLines
for statName in lineStatName:
self.stats[statName] = []
#Filling up count appropriately
for iter in ptxStats:
for statID in range(0, len(iter)):
if (iter[statID] != "Null"):
self.stats[lineStatName[statID]].append(int(iter[statID]))
def sum(self,key):
sum = 0
for iter in self.stats[key]:
sum += int(iter)
return sum
def takeMax(self,key):
try:
tmp = max(self.stats[key])
except:
tmp = 0
if cudaLineNo.debug:
print 'Exception in cudaLineNo.takeMax()', self.stats[key]
return tmp
def takeRatioSums(self, key1,key2):
tmp1 = float(self.sum(key1))
tmp2 = float(self.sum(key2))
try:
return tmp1/tmp2
except:
if cudaLineNo.debug:
print tmp1, tmp2
if tmp2 == 0 and cudaLineNo.debug:
print 'infinite'
return 0
class ptxLineNo:
debug = 0
def __init__(self, ptxStats):
self.stats = {}
for statID in range(0, len(ptxStats)):
self.stats[lineStatName[statID]] = int(ptxStats[statID])
def returnStat(self, key):
return self.stats[key]
def returnRatio(self, key1, key2):
tmp1 = float(self.stats[key1])
tmp2 = float(self.stats[key2])
try:
return tmp1/tmp2
except:
if tmp2 == 0 and ptxLineNo.debug:
print 'infinite'
return 0
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