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Modified parseHPMC.py to support local history and selecting between x-axis as bits or entries.

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Ross Thompson 2024-03-29 13:01:40 -05:00
parent 7c3e93bb2c
commit 129949b849
1 changed files with 54 additions and 30 deletions
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84
bin/parseHPMC.py
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@ -34,12 +34,12 @@ import numpy as np
import argparse import argparse
RefDataBP = [('twobitCModel6', 'twobitCModel', 64, 10.0060297551637), ('twobitCModel8', 'twobitCModel', 256, 8.4320392215602), ('twobitCModel10', 'twobitCModel', 1024, 7.29493318805151), RefDataBP = [('twobitCModel6', 'twobitCModel', 64, 128, 10.0060297551637), ('twobitCModel8', 'twobitCModel', 256, 512, 8.4320392215602), ('twobitCModel10', 'twobitCModel', 1024, 2048, 7.29493318805151),
('twobitCModel12', 'twobitCModel', 4096, 6.84739616147794), ('twobitCModel14', 'twobitCModel', 16384, 5.68432926870082), ('twobitCModel16', 'twobitCModel', 65536, 5.68432926870082), ('twobitCModel12', 'twobitCModel', 4096, 8192, 6.84739616147794), ('twobitCModel14', 'twobitCModel', 16384, 32768, 5.68432926870082), ('twobitCModel16', 'twobitCModel', 65536, 131072, 5.68432926870082),
('gshareCModel6', 'gshareCModel', 64, 11.4737703417701), ('gshareCModel8', 'gshareCModel', 256, 8.52341470761974), ('gshareCModel10', 'gshareCModel', 1024, 6.32975690693015), ('gshareCModel6', 'gshareCModel', 64, 128, 11.4737703417701), ('gshareCModel8', 'gshareCModel', 256, 512, 8.52341470761974), ('gshareCModel10', 'gshareCModel', 1024, 2048, 6.32975690693015),
('gshareCModel12', 'gshareCModel', 4096, 4.55424632377659), ('gshareCModel14', 'gshareCModel', 16384, 3.54251547725509), ('gshareCModel16', 'gshareCModel', 65536, 1.90424999467293)] ('gshareCModel12', 'gshareCModel', 4096, 8192, 4.55424632377659), ('gshareCModel14', 'gshareCModel', 16384, 32768, 3.54251547725509), ('gshareCModel16', 'gshareCModel', 65536, 131072, 1.90424999467293)]
RefDataBTB = [('BTBCModel6', 'BTBCModel', 64, 1.51480272475844), ('BTBCModel8', 'BTBCModel', 256, 0.209057900418965), ('BTBCModel10', 'BTBCModel', 1024, 0.0117345454469572), RefDataBTB = [('BTBCModel6', 'BTBCModel', 64, 128, 1.51480272475844), ('BTBCModel8', 'BTBCModel', 256, 512, 0.209057900418965), ('BTBCModel10', 'BTBCModel', 1024, 2048, 0.0117345454469572),
('BTBCModel12', 'BTBCModel', 4096, 0.00125540990359826), ('BTBCModel14', 'BTBCModel', 16384, 0.000732471628510962), ('BTBCModel16', 'BTBCModel', 65536, 0.000732471628510962)] ('BTBCModel12', 'BTBCModel', 4096, 8192, 0.00125540990359826), ('BTBCModel14', 'BTBCModel', 16384, 32768, 0.000732471628510962), ('BTBCModel16', 'BTBCModel', 65536, 131072, 0.000732471628510962)]
def ParseBranchListFile(path): def ParseBranchListFile(path):
'''Take the path to the list of Questa Sim log files containing the performance counters outputs. File '''Take the path to the list of Questa Sim log files containing the performance counters outputs. File
@ -120,25 +120,45 @@ def ComputeGeometricAverage(benchmarks):
benchmarks.append(('Mean', '', AllAve)) benchmarks.append(('Mean', '', AllAve))
def GenerateName(predictorType, predictorParams): def GenerateName(predictorType, predictorParams):
if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'ras'): if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'ras' or predictorType == 'global'):
return predictorType + predictorParams[0] return predictorType + predictorParams[0]
elif(predictorParams == 'local'): elif(predictorType == 'local'):
return predictorType + predictorParams[0] + '_' + predictorParams[1] return predictorType + predictorParams[0] + '_' + predictorParams[1]
else: else:
print(f'Error unsupported predictor type {predictorType}') print(f'Error unsupported predictor type {predictorType}')
sys.exit(-1) sys.exit(-1)
def GenerateDisplayName(predictorType, predictorParams):
if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'ras' or predictorType == 'global'):
return predictorType
elif(predictorType == 'local'):
return predictorType + predictorParams[0]
else:
print(f'Error unsupported predictor type {predictorType}')
sys.exit(-1)
def ComputePredNumEntries(predictorType, predictorParams): def ComputePredNumEntries(predictorType, predictorParams):
if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class'): if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'global'):
return 2**int(predictorParams[0]) return 2**int(predictorParams[0])
elif(predictorType == 'ras'): elif(predictorType == 'ras'):
return int(predictorParams[0]) return int(predictorParams[0])
elif(predictorParams == 'local'): elif(predictorType == 'local'):
return 2**int(predictorParams[0]) * int(predictorParams[1]) + 2**int(predictorParams[1]) return 2**int(predictorParams[0]) * int(predictorParams[1]) + 2**int(predictorParams[1])
else: else:
print(f'Error unsupported predictor type {predictorType}') print(f'Error unsupported predictor type {predictorType}')
sys.exit(-1) sys.exit(-1)
def ComputePredSize(predictorType, predictorParams):
if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'global'):
return 2*2**int(predictorParams[0])
elif(predictorType == 'ras'):
return int(predictorParams[0])
elif(predictorType == 'local'):
return 2**int(predictorParams[0]) * int(predictorParams[1]) + 2*2**int(predictorParams[1])
else:
print(f'Error unsupported predictor type {predictorType}')
sys.exit(-1)
def BuildDataBase(predictorLogs): def BuildDataBase(predictorLogs):
# Once done with the following loop, performanceCounterList will contain the predictor type and size along with the # Once done with the following loop, performanceCounterList will contain the predictor type and size along with the
# raw performance counter data and the processed data on a per benchmark basis. It also includes the geometric mean. # raw performance counter data and the processed data on a per benchmark basis. It also includes the geometric mean.
@ -164,16 +184,16 @@ def BuildDataBase(predictorLogs):
ComputeStats(performanceCounters) ComputeStats(performanceCounters)
ComputeGeometricAverage(performanceCounters) ComputeGeometricAverage(performanceCounters)
#print(performanceCounters) #print(performanceCounters)
performanceCounterList.append([GenerateName(predictorType, predictorParams), predictorType, performanceCounters, ComputePredNumEntries(predictorType, predictorParams)]) performanceCounterList.append([GenerateName(predictorType, predictorParams), GenerateDisplayName(predictorType, predictorParams), performanceCounters, ComputePredNumEntries(predictorType, predictorParams), ComputePredSize(predictorType, predictorParams)])
return performanceCounterList return performanceCounterList
def ReorderDataBase(performanceCounterList): def ReorderDataBase(performanceCounterList):
# Reorder the data so the benchmark name comes first, then the branch predictor configuration # Reorder the data so the benchmark name comes first, then the branch predictor configuration
benchmarkFirstList = [] benchmarkFirstList = []
for (predictorName, predictorPrefixName, benchmarks, entries) in performanceCounterList: for (predictorName, predictorPrefixName, benchmarks, entries, size) in performanceCounterList:
for benchmark in benchmarks: for benchmark in benchmarks:
(nameString, opt, dataDict) = benchmark (nameString, opt, dataDict) = benchmark
benchmarkFirstList.append((nameString, opt, predictorName, predictorPrefixName, entries, dataDict)) benchmarkFirstList.append((nameString, opt, predictorName, predictorPrefixName, entries, size, dataDict))
return benchmarkFirstList return benchmarkFirstList
def ExtractSelectedData(benchmarkFirstList): def ExtractSelectedData(benchmarkFirstList):
@ -181,7 +201,8 @@ def ExtractSelectedData(benchmarkFirstList):
# namestring + opt, config # namestring + opt, config
benchmarkDict = { } benchmarkDict = { }
for benchmark in benchmarkFirstList: for benchmark in benchmarkFirstList:
(name, opt, config, prefixName, entries, dataDict) = benchmark (name, opt, config, prefixName, entries, size, dataDict) = benchmark
#print(f'config = {config}, prefixName = {prefixName} entries = {entries}')
# use this code to distinguish speed opt and size opt. # use this code to distinguish speed opt and size opt.
#if opt == 'bd_speedopt_speed': NewName = name+'Sp' #if opt == 'bd_speedopt_speed': NewName = name+'Sp'
#elif opt == 'bd_sizeopt_speed': NewName = name+'Sz' #elif opt == 'bd_sizeopt_speed': NewName = name+'Sz'
@ -190,18 +211,19 @@ def ExtractSelectedData(benchmarkFirstList):
#print(NewName) #print(NewName)
#NewName = name+'_'+opt #NewName = name+'_'+opt
if NewName in benchmarkDict: if NewName in benchmarkDict:
benchmarkDict[NewName].append((config, prefixName, entries, dataDict[ReportPredictorType])) benchmarkDict[NewName].append((config, prefixName, entries, size, dataDict[ReportPredictorType]))
else: else:
benchmarkDict[NewName] = [(config, prefixName, entries, dataDict[ReportPredictorType])] benchmarkDict[NewName] = [(config, prefixName, entries, size, dataDict[ReportPredictorType])]
return benchmarkDict return benchmarkDict
def ReportAsTable(benchmarkDict): def ReportAsTable(benchmarkDict):
refLine = benchmarkDict['Mean'] refLine = benchmarkDict['Mean']
FirstLine = [] FirstLine = []
SecondLine = [] SecondLine = []
for (name, typ, size, val) in refLine: for Elements in refLine:
(name, typ, size, entries, val) = Elements
FirstLine.append(name) FirstLine.append(name)
SecondLine.append(size) SecondLine.append(entries if not args.size else size)
sys.stdout.write('benchmark\t\t') sys.stdout.write('benchmark\t\t')
for name in FirstLine: for name in FirstLine:
@ -216,7 +238,7 @@ def ReportAsTable(benchmarkDict):
if(args.summary): if(args.summary):
sys.stdout.write('Mean\t\t\t') sys.stdout.write('Mean\t\t\t')
for (name, typ, size, val) in refLine: for (name, typ, size, entries, val) in refLine:
sys.stdout.write('%0.2f\t\t' % (val if not args.invert else 100 - val)) sys.stdout.write('%0.2f\t\t' % (val if not args.invert else 100 - val))
sys.stdout.write('\n') sys.stdout.write('\n')
@ -226,7 +248,7 @@ def ReportAsTable(benchmarkDict):
if(length < 8): sys.stdout.write('%s\t\t\t' % benchmark) if(length < 8): sys.stdout.write('%s\t\t\t' % benchmark)
elif(length < 16): sys.stdout.write('%s\t\t' % benchmark) elif(length < 16): sys.stdout.write('%s\t\t' % benchmark)
else: sys.stdout.write('%s\t' % benchmark) else: sys.stdout.write('%s\t' % benchmark)
for (name, typ, size, val) in benchmarkDict[benchmark]: for (name, typ, entries, size, val) in benchmarkDict[benchmark]:
sys.stdout.write('%0.2f\t\t' % (val if not args.invert else 100 -val)) sys.stdout.write('%0.2f\t\t' % (val if not args.invert else 100 -val))
sys.stdout.write('\n') sys.stdout.write('\n')
@ -234,14 +256,14 @@ def ReportAsText(benchmarkDict):
if(args.summary): if(args.summary):
mean = benchmarkDict['Mean'] mean = benchmarkDict['Mean']
print('Mean') print('Mean')
for (name, typ, size, val) in mean: for (name, typ, entries. size, val) in mean:
sys.stdout.write('%s %s %0.2f\n' % (name, size, val if not args.invert else 100 - val)) sys.stdout.write('%s %s %0.2f\n' % (name, entries if not args.size else size, val if not args.invert else 100 - val))
if(not args.summary): if(not args.summary):
for benchmark in benchmarkDict: for benchmark in benchmarkDict:
print(benchmark) print(benchmark)
for (name, type, size, val) in benchmarkDict[benchmark]: for (name, type, entries, size, val) in benchmarkDict[benchmark]:
sys.stdout.write('%s %s %0.2f\n' % (name, size, val if not args.invert else 100 - val)) sys.stdout.write('%s %s %0.2f\n' % (name, entries if not args.size else size, val if not args.invert else 100 - val))
def Inversion(lst): def Inversion(lst):
return [x if not args.invert else 100 - x for x in lst] return [x if not args.invert else 100 - x for x in lst]
@ -306,11 +328,11 @@ def ReportAsGraph(benchmarkDict, bar, FileName):
# branch predictors with various parameterizations # branch predictors with various parameterizations
# group the parameterizations by the common typ. # group the parameterizations by the common typ.
sequencies = {} sequencies = {}
for (name, typ, size, value) in benchmarkDict['Mean']: for (name, typ, entries, size, value) in benchmarkDict['Mean']:
if not typ in sequencies: if not typ in sequencies:
sequencies[typ] = [(size, value)] sequencies[typ] = [(entries if not args.size else size, value)]
else: else:
sequencies[typ].append((size,value)) sequencies[typ].append((entries if not args.size else size,value))
# then graph the common typ as a single line+scatter plot # then graph the common typ as a single line+scatter plot
# finally repeat for all typs of branch predictors and overlay # finally repeat for all typs of branch predictors and overlay
fig, axes = plt.subplots() fig, axes = plt.subplots()
@ -327,7 +349,8 @@ def ReportAsGraph(benchmarkDict, bar, FileName):
axes.legend(loc='upper left') axes.legend(loc='upper left')
axes.set_xscale("log") axes.set_xscale("log")
axes.set_ylabel('Prediction Accuracy') axes.set_ylabel('Prediction Accuracy')
axes.set_xlabel('Entries') Xlabel = 'Entries' if not args.size else 'Size (bits)'
axes.set_xlabel(Xlabel)
axes.set_xticks(xdata) axes.set_xticks(xdata)
axes.set_xticklabels(xdata) axes.set_xticklabels(xdata)
axes.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5) axes.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
@ -368,7 +391,7 @@ def ReportAsGraph(benchmarkDict, bar, FileName):
for benchmarkName in benchmarkDict: for benchmarkName in benchmarkDict:
currBenchmark = benchmarkDict[benchmarkName] currBenchmark = benchmarkDict[benchmarkName]
xlabelList.append(benchmarkName) xlabelList.append(benchmarkName)
for (name, typ, size, value) in currBenchmark: for (name, typ, entries, size, value) in currBenchmark:
if(name not in seriesDict): if(name not in seriesDict):
seriesDict[name] = [value] seriesDict[name] = [value]
else: else:
@ -381,7 +404,7 @@ def ReportAsGraph(benchmarkDict, bar, FileName):
for benchmarkName in benchmarkDict: for benchmarkName in benchmarkDict:
currBenchmark = benchmarkDict[benchmarkName] currBenchmark = benchmarkDict[benchmarkName]
xlabelListBig.append(benchmarkName) xlabelListBig.append(benchmarkName)
for (name, typ, size, value) in currBenchmark: for (name, typ, entries, size, value) in currBenchmark:
if(name not in seriesDictBig): if(name not in seriesDictBig):
seriesDictBig[name] = [value] seriesDictBig[name] = [value]
else: else:
@ -410,6 +433,7 @@ parser.add_argument('-s', '--summary', action='store_const', help='Show only the
parser.add_argument('-b', '--bar', action='store_const', help='Plot graphs.', default=False, const=True) parser.add_argument('-b', '--bar', action='store_const', help='Plot graphs.', default=False, const=True)
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) 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)
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) 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)
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)
displayMode = parser.add_mutually_exclusive_group() displayMode = parser.add_mutually_exclusive_group()
displayMode.add_argument('--text', action='store_const', help='Display in text format only.', default=False, const=True) displayMode.add_argument('--text', action='store_const', help='Display in text format only.', default=False, const=True)