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https://github.com/openhwgroup/cvw
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Modified parseHPMC.py to support local history and selecting between x-axis as bits or entries.
This commit is contained in:
Ross Thompson
parent
7c3e93bb2c
commit
129949b849
@ -34,12 +34,12 @@ import numpy as np
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import argparse
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RefDataBP = [('twobitCModel6', 'twobitCModel', 64, 10.0060297551637), ('twobitCModel8', 'twobitCModel', 256, 8.4320392215602), ('twobitCModel10', 'twobitCModel', 1024, 7.29493318805151),
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('twobitCModel12', 'twobitCModel', 4096, 6.84739616147794), ('twobitCModel14', 'twobitCModel', 16384, 5.68432926870082), ('twobitCModel16', 'twobitCModel', 65536, 5.68432926870082),
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('gshareCModel6', 'gshareCModel', 64, 11.4737703417701), ('gshareCModel8', 'gshareCModel', 256, 8.52341470761974), ('gshareCModel10', 'gshareCModel', 1024, 6.32975690693015),
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('gshareCModel12', 'gshareCModel', 4096, 4.55424632377659), ('gshareCModel14', 'gshareCModel', 16384, 3.54251547725509), ('gshareCModel16', 'gshareCModel', 65536, 1.90424999467293)]
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RefDataBTB = [('BTBCModel6', 'BTBCModel', 64, 1.51480272475844), ('BTBCModel8', 'BTBCModel', 256, 0.209057900418965), ('BTBCModel10', 'BTBCModel', 1024, 0.0117345454469572),
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('BTBCModel12', 'BTBCModel', 4096, 0.00125540990359826), ('BTBCModel14', 'BTBCModel', 16384, 0.000732471628510962), ('BTBCModel16', 'BTBCModel', 65536, 0.000732471628510962)]
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RefDataBP = [('twobitCModel6', 'twobitCModel', 64, 128, 10.0060297551637), ('twobitCModel8', 'twobitCModel', 256, 512, 8.4320392215602), ('twobitCModel10', 'twobitCModel', 1024, 2048, 7.29493318805151),
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('twobitCModel12', 'twobitCModel', 4096, 8192, 6.84739616147794), ('twobitCModel14', 'twobitCModel', 16384, 32768, 5.68432926870082), ('twobitCModel16', 'twobitCModel', 65536, 131072, 5.68432926870082),
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('gshareCModel6', 'gshareCModel', 64, 128, 11.4737703417701), ('gshareCModel8', 'gshareCModel', 256, 512, 8.52341470761974), ('gshareCModel10', 'gshareCModel', 1024, 2048, 6.32975690693015),
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('gshareCModel12', 'gshareCModel', 4096, 8192, 4.55424632377659), ('gshareCModel14', 'gshareCModel', 16384, 32768, 3.54251547725509), ('gshareCModel16', 'gshareCModel', 65536, 131072, 1.90424999467293)]
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RefDataBTB = [('BTBCModel6', 'BTBCModel', 64, 128, 1.51480272475844), ('BTBCModel8', 'BTBCModel', 256, 512, 0.209057900418965), ('BTBCModel10', 'BTBCModel', 1024, 2048, 0.0117345454469572),
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('BTBCModel12', 'BTBCModel', 4096, 8192, 0.00125540990359826), ('BTBCModel14', 'BTBCModel', 16384, 32768, 0.000732471628510962), ('BTBCModel16', 'BTBCModel', 65536, 131072, 0.000732471628510962)]
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def ParseBranchListFile(path):
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'''Take the path to the list of Questa Sim log files containing the performance counters outputs. File
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@ -120,25 +120,45 @@ def ComputeGeometricAverage(benchmarks):
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benchmarks.append(('Mean', '', AllAve))
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def GenerateName(predictorType, predictorParams):
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if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'ras'):
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if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'ras' or predictorType == 'global'):
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return predictorType + predictorParams[0]
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elif(predictorParams == 'local'):
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elif(predictorType == 'local'):
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return predictorType + predictorParams[0] + '_' + predictorParams[1]
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else:
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print(f'Error unsupported predictor type {predictorType}')
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sys.exit(-1)
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def GenerateDisplayName(predictorType, predictorParams):
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if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'ras' or predictorType == 'global'):
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return predictorType
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elif(predictorType == 'local'):
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return predictorType + predictorParams[0]
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else:
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print(f'Error unsupported predictor type {predictorType}')
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sys.exit(-1)
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def ComputePredNumEntries(predictorType, predictorParams):
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if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class'):
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if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'global'):
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return 2**int(predictorParams[0])
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elif(predictorType == 'ras'):
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return int(predictorParams[0])
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elif(predictorParams == 'local'):
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elif(predictorType == 'local'):
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return 2**int(predictorParams[0]) * int(predictorParams[1]) + 2**int(predictorParams[1])
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else:
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print(f'Error unsupported predictor type {predictorType}')
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sys.exit(-1)
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def ComputePredSize(predictorType, predictorParams):
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if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'global'):
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return 2*2**int(predictorParams[0])
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elif(predictorType == 'ras'):
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return int(predictorParams[0])
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elif(predictorType == 'local'):
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return 2**int(predictorParams[0]) * int(predictorParams[1]) + 2*2**int(predictorParams[1])
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else:
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print(f'Error unsupported predictor type {predictorType}')
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sys.exit(-1)
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def BuildDataBase(predictorLogs):
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# Once done with the following loop, performanceCounterList will contain the predictor type and size along with the
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# raw performance counter data and the processed data on a per benchmark basis. It also includes the geometric mean.
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@ -164,16 +184,16 @@ def BuildDataBase(predictorLogs):
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ComputeStats(performanceCounters)
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ComputeGeometricAverage(performanceCounters)
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#print(performanceCounters)
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performanceCounterList.append([GenerateName(predictorType, predictorParams), predictorType, performanceCounters, ComputePredNumEntries(predictorType, predictorParams)])
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performanceCounterList.append([GenerateName(predictorType, predictorParams), GenerateDisplayName(predictorType, predictorParams), performanceCounters, ComputePredNumEntries(predictorType, predictorParams), ComputePredSize(predictorType, predictorParams)])
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return performanceCounterList
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def ReorderDataBase(performanceCounterList):
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# Reorder the data so the benchmark name comes first, then the branch predictor configuration
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benchmarkFirstList = []
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for (predictorName, predictorPrefixName, benchmarks, entries) in performanceCounterList:
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for (predictorName, predictorPrefixName, benchmarks, entries, size) in performanceCounterList:
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for benchmark in benchmarks:
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(nameString, opt, dataDict) = benchmark
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benchmarkFirstList.append((nameString, opt, predictorName, predictorPrefixName, entries, dataDict))
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benchmarkFirstList.append((nameString, opt, predictorName, predictorPrefixName, entries, size, dataDict))
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return benchmarkFirstList
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def ExtractSelectedData(benchmarkFirstList):
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@ -181,7 +201,8 @@ def ExtractSelectedData(benchmarkFirstList):
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# namestring + opt, config
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benchmarkDict = { }
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for benchmark in benchmarkFirstList:
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(name, opt, config, prefixName, entries, dataDict) = benchmark
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(name, opt, config, prefixName, entries, size, dataDict) = benchmark
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#print(f'config = {config}, prefixName = {prefixName} entries = {entries}')
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# use this code to distinguish speed opt and size opt.
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#if opt == 'bd_speedopt_speed': NewName = name+'Sp'
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#elif opt == 'bd_sizeopt_speed': NewName = name+'Sz'
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@ -190,18 +211,19 @@ def ExtractSelectedData(benchmarkFirstList):
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#print(NewName)
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#NewName = name+'_'+opt
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if NewName in benchmarkDict:
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benchmarkDict[NewName].append((config, prefixName, entries, dataDict[ReportPredictorType]))
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benchmarkDict[NewName].append((config, prefixName, entries, size, dataDict[ReportPredictorType]))
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else:
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benchmarkDict[NewName] = [(config, prefixName, entries, dataDict[ReportPredictorType])]
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benchmarkDict[NewName] = [(config, prefixName, entries, size, dataDict[ReportPredictorType])]
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return benchmarkDict
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def ReportAsTable(benchmarkDict):
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refLine = benchmarkDict['Mean']
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FirstLine = []
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SecondLine = []
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for (name, typ, size, val) in refLine:
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for Elements in refLine:
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(name, typ, size, entries, val) = Elements
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FirstLine.append(name)
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SecondLine.append(size)
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SecondLine.append(entries if not args.size else size)
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sys.stdout.write('benchmark\t\t')
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for name in FirstLine:
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@ -216,7 +238,7 @@ def ReportAsTable(benchmarkDict):
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if(args.summary):
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sys.stdout.write('Mean\t\t\t')
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for (name, typ, size, val) in refLine:
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for (name, typ, size, entries, val) in refLine:
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sys.stdout.write('%0.2f\t\t' % (val if not args.invert else 100 - val))
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sys.stdout.write('\n')
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@ -226,7 +248,7 @@ def ReportAsTable(benchmarkDict):
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if(length < 8): sys.stdout.write('%s\t\t\t' % benchmark)
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elif(length < 16): sys.stdout.write('%s\t\t' % benchmark)
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else: sys.stdout.write('%s\t' % benchmark)
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for (name, typ, size, val) in benchmarkDict[benchmark]:
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for (name, typ, entries, size, val) in benchmarkDict[benchmark]:
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sys.stdout.write('%0.2f\t\t' % (val if not args.invert else 100 -val))
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sys.stdout.write('\n')
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@ -234,14 +256,14 @@ def ReportAsText(benchmarkDict):
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if(args.summary):
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mean = benchmarkDict['Mean']
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print('Mean')
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for (name, typ, size, val) in mean:
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sys.stdout.write('%s %s %0.2f\n' % (name, size, val if not args.invert else 100 - val))
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for (name, typ, entries. size, val) in mean:
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sys.stdout.write('%s %s %0.2f\n' % (name, entries if not args.size else size, val if not args.invert else 100 - val))
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if(not args.summary):
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for benchmark in benchmarkDict:
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print(benchmark)
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for (name, type, size, val) in benchmarkDict[benchmark]:
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sys.stdout.write('%s %s %0.2f\n' % (name, size, val if not args.invert else 100 - val))
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for (name, type, entries, size, val) in benchmarkDict[benchmark]:
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sys.stdout.write('%s %s %0.2f\n' % (name, entries if not args.size else size, val if not args.invert else 100 - val))
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def Inversion(lst):
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return [x if not args.invert else 100 - x for x in lst]
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@ -306,11 +328,11 @@ def ReportAsGraph(benchmarkDict, bar, FileName):
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# branch predictors with various parameterizations
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# group the parameterizations by the common typ.
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sequencies = {}
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for (name, typ, size, value) in benchmarkDict['Mean']:
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for (name, typ, entries, size, value) in benchmarkDict['Mean']:
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if not typ in sequencies:
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sequencies[typ] = [(size, value)]
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sequencies[typ] = [(entries if not args.size else size, value)]
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else:
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sequencies[typ].append((size,value))
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sequencies[typ].append((entries if not args.size else size,value))
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# then graph the common typ as a single line+scatter plot
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# finally repeat for all typs of branch predictors and overlay
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fig, axes = plt.subplots()
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@ -327,7 +349,8 @@ def ReportAsGraph(benchmarkDict, bar, FileName):
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axes.legend(loc='upper left')
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axes.set_xscale("log")
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axes.set_ylabel('Prediction Accuracy')
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axes.set_xlabel('Entries')
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Xlabel = 'Entries' if not args.size else 'Size (bits)'
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axes.set_xlabel(Xlabel)
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axes.set_xticks(xdata)
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axes.set_xticklabels(xdata)
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axes.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
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@ -368,7 +391,7 @@ def ReportAsGraph(benchmarkDict, bar, FileName):
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for benchmarkName in benchmarkDict:
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currBenchmark = benchmarkDict[benchmarkName]
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xlabelList.append(benchmarkName)
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for (name, typ, size, value) in currBenchmark:
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for (name, typ, entries, size, value) in currBenchmark:
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if(name not in seriesDict):
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seriesDict[name] = [value]
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else:
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@ -381,7 +404,7 @@ def ReportAsGraph(benchmarkDict, bar, FileName):
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for benchmarkName in benchmarkDict:
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currBenchmark = benchmarkDict[benchmarkName]
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xlabelListBig.append(benchmarkName)
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for (name, typ, size, value) in currBenchmark:
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for (name, typ, entries, size, value) in currBenchmark:
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if(name not in seriesDictBig):
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seriesDictBig[name] = [value]
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else:
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@ -410,6 +433,7 @@ parser.add_argument('-s', '--summary', action='store_const', help='Show only the
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parser.add_argument('-b', '--bar', action='store_const', help='Plot graphs.', default=False, const=True)
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parser.add_argument('-g', '--reference', action='store_const', help='Include the golden reference model from branch-predictor-simulator. Data stored statically at the top of %(prog)s. If you need to regenreate use CModelBranchAcurracy.sh', default=False, const=True)
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parser.add_argument('-i', '--invert', action='store_const', help='Invert metric. Example Branch miss prediction becomes prediction accuracy. 100 - miss rate', default=False, const=True)
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parser.add_argument('--size', action='store_const', help='Display x-axis as size in bits rather than number of table entries', default=False, const=True)
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displayMode = parser.add_mutually_exclusive_group()
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displayMode.add_argument('--text', action='store_const', help='Display in text format only.', default=False, const=True)
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