#!/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? self.initialized = 0 self.sampleNum = 0 # 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, 'sparse': 5} organize_str = {'scalar': 'scalar', 'implicit': 'impVec', 'index': 'idxVec', 'index2d': 'idx2DVec', 'sparse': 'sparse'} #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 == 'stackbar') elif (self.type == 4): assert(self.organize == 'idx2DVec') elif (self.type == 5): assert(self.organize == 'sparse') except Exception, (e): print "Error in creating new stat variable from string: %s" % string_spec raise e def initSparseMatrix(self): if (self.initialized == 0): if (self.type != 5): raise Exception("initSparseMatrix called from wrong variable type") self.data = [[], [], []] self.initialized = 1 self.sampleNum = 1 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