mirror of
https://github.com/openhwgroup/cvw
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Renamed parseTest.py to parseHPMC.py
This commit is contained in:
Ross Thompson
parent
9ae6261e5c
commit
293fa17eee
614
bin/parseHPMC.py
614
bin/parseHPMC.py
@ -1,8 +1,8 @@
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#!/usr/bin/python3
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###########################################
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## Written: Ross Thompson ross1728@gmail.com
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## Created: 4 Jan 2022
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## Written: Rose Thompson ross1728@gmail.com
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## Created: 20 September 2023
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## Modified:
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##
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## Purpose: Parses the performance counters from a modelsim trace.
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@ -28,110 +28,29 @@
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import os
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import sys
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import matplotlib.pyplot as plt
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import re
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import math
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import numpy as np
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import argparse
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#RefData={'twobitCModel' :(['6', '8', '10', '12', '14', '16'],
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# [11.0680836450622, 8.53864970807778, 7.59565430177984, 6.38741598498948, 5.83662961500838, 5.83662961500838]),
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# 'gshareCModel' : (['6', '8', '10', '12', '14', '16'],
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# [14.5859173702079, 12.3634674403619, 10.5806018170154, 8.38831266973592, 6.37097544620762, 3.52638362703015])
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#}
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RefData = [('twobitCModel6', 'twobitCModel', 64, 9.65280765420711), ('twobitCModel8', 'twobitCModel', 256, 8.75120245829945), ('twobitCModel10', 'twobitCModel', 1024, 8.1318382397263),
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('twobitCModel12', 'twobitCModel', 4096, 7.53026646633342), ('twobitCModel14', 'twobitCModel', 16384, 6.07679338544009), ('twobitCModel16', 'twobitCModel', 65536, 6.07679338544009),
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('gshareCModel6', 'gshareCModel', 64, 10.6602835418646), ('gshareCModel8', 'gshareCModel', 256, 8.38384710559667), ('gshareCModel10', 'gshareCModel', 1024, 6.36847432155534),
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('gshareCModel12', 'gshareCModel', 4096, 3.91108491151983), ('gshareCModel14', 'gshareCModel', 16384, 2.83926519215395), ('gshareCModel16', 'gshareCModel', 65536, .60213659066941)]
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#RefData = [('twobitCModel6', 11.0501534891674), ('twobitCModel8', 8.51829052266352), ('twobitCModel10', 7.56775222626483),
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# ('twobitCModel12', 6.31366834586515), ('twobitCModel14', 5.72699936834177), ('twobitCModel16', 5.72699936834177),
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# ('gshareCModel6', 14.5731555979574), ('gshareCModel8', 12.3155658100497), ('gshareCModel10', 10.4589596630561),
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# ('gshareCModel12', 8.25796055444401), ('gshareCModel14', 6.23093702707613), ('gshareCModel16', 3.34001125650374)]
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RefData = [('twobitCModel6', 9.65280765420711), ('twobitCModel8', 8.75120245829945), ('twobitCModel10', 8.1318382397263),
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('twobitCModel12', 7.53026646633342), ('twobitCModel14', 6.07679338544009), ('twobitCModel16', 6.07679338544009),
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('gshareCModel6', 10.6602835418646), ('gshareCModel8', 8.38384710559667), ('gshareCModel10', 6.36847432155534),
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('gshareCModel12', 3.91108491151983), ('gshareCModel14', 2.83926519215395), ('gshareCModel16', .60213659066941)]
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def ComputeCPI(benchmark):
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'Computes and inserts CPI into benchmark stats.'
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(nameString, opt, dataDict) = benchmark
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CPI = 1.0 * int(dataDict['Mcycle']) / int(dataDict['InstRet'])
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dataDict['CPI'] = CPI
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def ComputeBranchDirMissRate(benchmark):
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'Computes and inserts branch direction miss prediction rate.'
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(nameString, opt, dataDict) = benchmark
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branchDirMissRate = 100.0 * int(dataDict['BP Dir Wrong']) / int(dataDict['Br Count'])
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dataDict['BDMR'] = branchDirMissRate
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def ComputeBranchTargetMissRate(benchmark):
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'Computes and inserts branch target miss prediction rate.'
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# *** this is wrong in the verilog test bench
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(nameString, opt, dataDict) = benchmark
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branchTargetMissRate = 100.0 * int(dataDict['BP Target Wrong']) / (int(dataDict['Br Count']) + int(dataDict['Jump Not Return']))
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dataDict['BTMR'] = branchTargetMissRate
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def ComputeRASMissRate(benchmark):
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'Computes and inserts return address stack miss prediction rate.'
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(nameString, opt, dataDict) = benchmark
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RASMPR = 100.0 * int(dataDict['RAS Wrong']) / int(dataDict['Return'])
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dataDict['RASMPR'] = RASMPR
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def ComputeInstrClassMissRate(benchmark):
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'Computes and inserts instruction class miss prediction rate.'
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(nameString, opt, dataDict) = benchmark
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ClassMPR = 100.0 * int(dataDict['Instr Class Wrong']) / int(dataDict['InstRet'])
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dataDict['ClassMPR'] = ClassMPR
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def ComputeICacheMissRate(benchmark):
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'Computes and inserts instruction class miss prediction rate.'
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(nameString, opt, dataDict) = benchmark
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ICacheMR = 100.0 * int(dataDict['I Cache Miss']) / int(dataDict['I Cache Access'])
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dataDict['ICacheMR'] = ICacheMR
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def ComputeICacheMissTime(benchmark):
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'Computes and inserts instruction class miss prediction rate.'
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(nameString, opt, dataDict) = benchmark
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cycles = int(dataDict['I Cache Miss'])
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if(cycles == 0): ICacheMR = 0
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else: ICacheMR = 100.0 * int(dataDict['I Cache Cycles']) / cycles
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dataDict['ICacheMT'] = ICacheMR
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def ComputeDCacheMissRate(benchmark):
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'Computes and inserts instruction class miss prediction rate.'
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(nameString, opt, dataDict) = benchmark
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DCacheMR = 100.0 * int(dataDict['D Cache Miss']) / int(dataDict['D Cache Access'])
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dataDict['DCacheMR'] = DCacheMR
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def ComputeDCacheMissTime(benchmark):
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'Computes and inserts instruction class miss prediction rate.'
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(nameString, opt, dataDict) = benchmark
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cycles = int(dataDict['D Cache Miss'])
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if(cycles == 0): DCacheMR = 0
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else: DCacheMR = 100.0 * int(dataDict['D Cache Cycles']) / cycles
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dataDict['DCacheMT'] = DCacheMR
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def ComputeAll(benchmarks):
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for benchmark in benchmarks:
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ComputeCPI(benchmark)
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ComputeBranchDirMissRate(benchmark)
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ComputeBranchTargetMissRate(benchmark)
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ComputeRASMissRate(benchmark)
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ComputeInstrClassMissRate(benchmark)
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ComputeICacheMissRate(benchmark)
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ComputeICacheMissTime(benchmark)
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ComputeDCacheMissRate(benchmark)
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ComputeDCacheMissTime(benchmark)
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def printStats(benchmark):
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(nameString, opt, dataDict) = benchmark
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print('Test', nameString)
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print('Compile configuration', opt)
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print('CPI \t\t\t %1.2f' % dataDict['CPI'])
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print('Branch Dir Pred Miss Rate %2.2f' % dataDict['BDMR'])
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print('Branch Target Pred Miss Rate %2.2f' % dataDict['BTMR'])
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print('RAS Miss Rate \t\t %1.2f' % dataDict['RASMPR'])
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print('Instr Class Miss Rate %1.2f' % dataDict['ClassMPR'])
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print('I Cache Miss Rate %1.4f' % dataDict['ICacheMR'])
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print('I Cache Miss Ave Cycles %1.4f' % dataDict['ICacheMT'])
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print('D Cache Miss Rate %1.4f' % dataDict['DCacheMR'])
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print('D Cache Miss Ave Cycles %1.4f' % dataDict['DCacheMT'])
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print()
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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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is formated in row columns. Each row is a trace with the file, branch predictor type, and the parameters.
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parameters can be any number and depend on the predictor type. Returns a list of lists.'''
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lst = []
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BranchList = open(path, 'r')
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for line in BranchList:
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tokens = line.split()
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predictorLog = os.path.dirname(path) + '/' + tokens[0]
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predictorType = tokens[1]
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predictorParams = tokens[2::]
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lst.append([predictorLog, predictorType, predictorParams])
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#print(predictorLog, predictorType, predictorParams)
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return lst
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def ProcessFile(fileName):
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'''Extract preformance counters from a modelsim log. Outputs a list of tuples for each test/benchmark.
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@ -150,43 +69,37 @@ def ProcessFile(fileName):
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HPMClist = { }
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elif(len(lineToken) > 4 and lineToken[1][0:3] == 'Cnt'):
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countToken = line.split('=')[1].split()
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value = int(countToken[0])
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value = int(countToken[0]) if countToken[0] != 'x' else 0
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name = ' '.join(countToken[1:])
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HPMClist[name] = value
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elif ('is done' in line):
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benchmarks.append((testName, opt, HPMClist))
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return benchmarks
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def ComputeArithmeticAverage(benchmarks):
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average = {}
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index = 0
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for (testName, opt, HPMClist) in benchmarks:
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for field in HPMClist:
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value = HPMClist[field]
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if field not in average:
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average[field] = value
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else:
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average[field] += value
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index += 1
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benchmarks.append(('All', '', average))
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def FormatToPlot(currBenchmark):
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names = []
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values = []
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for config in currBenchmark:
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#print ('config' , config)
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names.append(config[0])
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values.append(config[1])
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return (names, values)
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def ComputeStats(benchmarks):
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for benchmark in benchmarks:
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(nameString, opt, dataDict) = benchmark
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dataDict['CPI'] = 1.0 * int(dataDict['Mcycle']) / int(dataDict['InstRet'])
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dataDict['BDMR'] = 100.0 * int(dataDict['BP Dir Wrong']) / int(dataDict['Br Count'])
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dataDict['BTMR'] = 100.0 * int(dataDict['BP Target Wrong']) / (int(dataDict['Br Count']) + int(dataDict['Jump Not Return']))
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dataDict['RASMPR'] = 100.0 * int(dataDict['RAS Wrong']) / int(dataDict['Return'])
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dataDict['ClassMPR'] = 100.0 * int(dataDict['Instr Class Wrong']) / int(dataDict['InstRet'])
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dataDict['ICacheMR'] = 100.0 * int(dataDict['I Cache Miss']) / int(dataDict['I Cache Access'])
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cycles = int(dataDict['I Cache Miss'])
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if(cycles == 0): ICacheMR = 0
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else: ICacheMR = 100.0 * int(dataDict['I Cache Cycles']) / cycles
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dataDict['ICacheMT'] = ICacheMR
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dataDict['DCacheMR'] = 100.0 * int(dataDict['D Cache Miss']) / int(dataDict['D Cache Access'])
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(nameString, opt, dataDict) = benchmark
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cycles = int(dataDict['D Cache Miss'])
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if(cycles == 0): DCacheMR = 0
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else: DCacheMR = 100.0 * int(dataDict['D Cache Cycles']) / cycles
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dataDict['DCacheMT'] = DCacheMR
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def GeometricAverage(benchmarks, field):
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Product = 1
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index = 0
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for (testName, opt, HPMCList) in benchmarks:
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#print(HPMCList)
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Product *= HPMCList[field]
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index += 1
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return Product ** (1.0/index)
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def ComputeGeometricAverage(benchmarks):
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fields = ['BDMR', 'BTMR', 'RASMPR', 'ClassMPR', 'ICacheMR', 'DCacheMR', 'CPI', 'ICacheMT', 'DCacheMT']
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@ -196,129 +109,362 @@ def ComputeGeometricAverage(benchmarks):
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index = 0
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for (testName, opt, HPMCList) in benchmarks:
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#print(HPMCList)
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Product *= HPMCList[field]
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value = HPMCList[field]
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if(value != 0): Product *= value # if that value is 0 exclude from mean because it destories the geo mean
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index += 1
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AllAve[field] = Product ** (1.0/index)
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benchmarks.append(('All', '', AllAve))
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benchmarks.append(('Mean', '', AllAve))
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if(sys.argv[1] == '-b'):
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configList = []
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summery = 0
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if(sys.argv[2] == '-s'):
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summery = 1
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sys.argv = sys.argv[1::]
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for config in sys.argv[2::]:
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benchmarks = ProcessFile(config)
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#ComputeArithmeticAverage(benchmarks)
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ComputeAll(benchmarks)
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ComputeGeometricAverage(benchmarks)
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#print('CONFIG: %s GEO MEAN: %f' % (config, GeometricAverage(benchmarks, 'BDMR')))
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configList.append((config.split('.')[0], benchmarks))
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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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return predictorType + predictorParams[0]
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elif(predictorParams == '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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# Merge all configruations into a single list
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benchmarkAll = []
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for (config, benchmarks) in configList:
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#print(config)
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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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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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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 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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# list
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# branch predictor configuration 0 (tuple)
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# benchmark name
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# compiler optimization
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# data (dictionary)
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# dictionary of performance counters
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# branch predictor configuration 1 (tuple)
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# benchmark name (dictionary)
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# compiler optimization
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# data
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# dictionary of performance counters
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# ...
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performanceCounterList = []
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for trace in predictorLogs:
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predictorLog = trace[0]
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predictorType = trace[1]
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predictorParams = trace[2]
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# Extract the performance counter data
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performanceCounters = ProcessFile(predictorLog)
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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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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 benchmark in benchmarks:
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(nameString, opt, dataDict) = benchmark
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#print("BENCHMARK")
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#print(nameString)
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#print(opt)
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#print(dataDict)
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benchmarkAll.append((nameString, opt, config, dataDict))
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#print('ALL!!!!!!!!!!')
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#for bench in benchmarkAll:
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# print('BENCHMARK')
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# print(bench)
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#print('ALL!!!!!!!!!!')
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benchmarkFirstList.append((nameString, opt, predictorName, predictorPrefixName, entries, dataDict))
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return benchmarkFirstList
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def ExtractSelectedData(benchmarkFirstList):
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# now extract all branch prediction direction miss rates for each
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# namestring + opt, config
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benchmarkDict = { }
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for benchmark in benchmarkAll:
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(name, opt, config, dataDict) = benchmark
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if name+'_'+opt in benchmarkDict:
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benchmarkDict[name+'_'+opt].append((config, dataDict['BDMR']))
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for benchmark in benchmarkFirstList:
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(name, opt, config, prefixName, entries, dataDict) = benchmark
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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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else: NewName = name
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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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else:
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benchmarkDict[name+'_'+opt] = [(config, dataDict['BDMR'])]
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benchmarkDict[NewName] = [(config, prefixName, entries, dataDict[ReportPredictorType])]
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return benchmarkDict
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size = len(benchmarkDict)
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index = 1
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if(summery == 0):
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#print('Number of plots', size)
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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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FirstLine.append(name)
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SecondLine.append(size)
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for benchmarkName in benchmarkDict:
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currBenchmark = benchmarkDict[benchmarkName]
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(names, values) = FormatToPlot(currBenchmark)
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print(names, values)
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plt.subplot(6, 7, index)
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plt.bar(names, values)
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plt.title(benchmarkName)
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plt.ylabel('BR Dir Miss Rate (%)')
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#plt.xlabel('Predictor')
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sys.stdout.write('benchmark\t\t')
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for name in FirstLine:
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if(len(name) < 8): sys.stdout.write('%s\t\t' % name)
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else: sys.stdout.write('%s\t' % name)
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sys.stdout.write('\n')
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sys.stdout.write('size\t\t\t')
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for size in SecondLine:
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if(len(str(size)) < 8): sys.stdout.write('%d\t\t' % size)
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else: sys.stdout.write('%d\t' % size)
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sys.stdout.write('\n')
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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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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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if(not args.summary):
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for benchmark in benchmarkDict:
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length = len(benchmark)
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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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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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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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||||
|
||||
if(not args.summary):
|
||||
for benchmark in benchmarkDict:
|
||||
print(benchmark)
|
||||
for (name, type, size, val) in benchmarkDict[benchmark]:
|
||||
sys.stdout.write('%s %s %0.2f\n' % (name, size, val if not args.invert else 100 - val))
|
||||
|
||||
def Inversion(lst):
|
||||
return [x if not args.invert else 100 - x for x in lst]
|
||||
|
||||
def BarGraph(seriesDict, xlabelList, BenchPerRow, FileName):
|
||||
index = 0
|
||||
NumberInGroup = len(seriesDict)
|
||||
# Figure out width of bars. NumberInGroup bars + want 2 bar space
|
||||
# the space between groups is 1
|
||||
EffectiveNumInGroup = NumberInGroup + 2
|
||||
barWidth = 1 / EffectiveNumInGroup
|
||||
fig = plt.subplots(figsize = (EffectiveNumInGroup*BenchPerRow/8, 4))
|
||||
colors = ['blue', 'blue', 'blue', 'blue', 'blue', 'blue', 'black', 'black', 'black', 'black', 'black', 'black']
|
||||
for name in seriesDict:
|
||||
xpos = np.arange(BenchPerRow)
|
||||
xpos = [x + index*barWidth for x in xpos]
|
||||
values = seriesDict[name]
|
||||
plt.bar(xpos, Inversion(values), width=barWidth, edgecolor='grey', label=name, color=colors[index%len(colors)])
|
||||
index += 1
|
||||
plt.xticks([r + barWidth*(NumberInGroup/2-0.5) for r in range(0, BenchPerRow)], xlabelList)
|
||||
plt.xlabel('Benchmark')
|
||||
if(not args.invert): plt.ylabel('Misprediction Rate (%)')
|
||||
else: plt.ylabel('Prediction Accuracy (%)')
|
||||
plt.legend(loc='upper left', ncol=2)
|
||||
plt.savefig(FileName)
|
||||
|
||||
def SelectPartition(xlabelListBig, seriesDictBig, group, BenchPerRow):
|
||||
seriesDictTrunk = {}
|
||||
for benchmarkName in seriesDictBig:
|
||||
lst = seriesDictBig[benchmarkName]
|
||||
seriesDictTrunk[benchmarkName] = lst[group*BenchPerRow:(group+1)*BenchPerRow]
|
||||
xlabelListTrunk = xlabelListBig[group*BenchPerRow:(group+1)*BenchPerRow]
|
||||
return(xlabelListTrunk, seriesDictTrunk)
|
||||
|
||||
|
||||
def ReportAsGraph(benchmarkDict, bar):
|
||||
def FormatToPlot(currBenchmark):
|
||||
names = []
|
||||
sizes = []
|
||||
values = []
|
||||
typs = []
|
||||
for config in currBenchmark:
|
||||
names.append(config[0])
|
||||
sizes.append(config[1])
|
||||
values.append(config[2])
|
||||
typs.append(config[3])
|
||||
return (names, sizes, values, typs)
|
||||
titlesInvert = {'BDMR' : 'Branch Direction Accuracy',
|
||||
'BTMR' : 'Branch Target Accuracy',
|
||||
'RASMPR': 'RAS Accuracy',
|
||||
'ClassMPR': 'Class Prediction Accuracy'}
|
||||
titles = {'BDMR' : 'Branch Direction Misprediction',
|
||||
'BTMR' : 'Branch Target Misprediction',
|
||||
'RASMPR': 'RAS Misprediction',
|
||||
'ClassMPR': 'Class Misprediction'}
|
||||
if(args.summary):
|
||||
markers = ['x', '.', '+', '*', '^', 'o', ',', 's']
|
||||
colors = ['blue', 'black', 'gray', 'dodgerblue', 'lightsteelblue', 'turquoise', 'black', 'blue']
|
||||
temp = benchmarkDict['Mean']
|
||||
|
||||
# the benchmarkDict['Mean'] contains sequencies of results for multiple
|
||||
# branch predictors with various parameterizations
|
||||
# group the parameterizations by the common typ.
|
||||
sequencies = {}
|
||||
for (name, typ, size, value) in benchmarkDict['Mean']:
|
||||
if not typ in sequencies:
|
||||
sequencies[typ] = [(size, value)]
|
||||
else:
|
||||
combined = benchmarkDict['All_']
|
||||
# merge the reference data into rtl data
|
||||
# combined.extend(RefData)
|
||||
(name, value) = FormatToPlot(combined)
|
||||
lst = []
|
||||
dct = {}
|
||||
category = []
|
||||
length = []
|
||||
accuracy = []
|
||||
for index in range(0, len(name)):
|
||||
match = re.match(r"([a-z]+)([0-9]+)", name[index], re.I)
|
||||
percent = 100 -value[index]
|
||||
if match:
|
||||
(PredType, size) = match.groups()
|
||||
category.append(PredType)
|
||||
length.append(size)
|
||||
accuracy.append(percent)
|
||||
if(PredType not in dct):
|
||||
dct[PredType] = ([size], [percent])
|
||||
else:
|
||||
(currSize, currPercent) = dct[PredType]
|
||||
currSize.append(size)
|
||||
currPercent.append(percent)
|
||||
dct[PredType] = (currSize, currPercent)
|
||||
print(dct)
|
||||
sequencies[typ].append((size,value))
|
||||
# then graph the common typ as a single line+scatter plot
|
||||
# finally repeat for all typs of branch predictors and overlay
|
||||
fig, axes = plt.subplots()
|
||||
marker={'twobit' : '^', 'gshare' : 'o', 'global' : 's', 'gshareBasic' : '*', 'globalBasic' : 'x', 'btb': 'x', 'twobitCModel' : 'x', 'gshareCModel' : '*', 'tenlocal' : '.', 'eightlocal' : ',', 'fourlocal' : 'x', 'tenlocalahead' : '.', 'eightlocalahead' : ',', 'fourlocalahead' : 'x', 'tenlocalrepair' : 'x'}
|
||||
colors={'twobit' : 'black', 'gshare' : 'blue', 'global' : 'dodgerblue', 'gshareBasic' : 'turquoise', 'globalBasic' : 'lightsteelblue', 'btb' : 'blue', 'twobitCModel' : 'gray', 'gshareCModel' : 'dodgerblue', 'tenlocal' : 'lightblue', 'eightlocal' : 'lightblue', 'fourlocal' : 'lightblue', 'tenlocalahead' : 'lightblue', 'eightlocalahead' : 'lightblue', 'fourlocalahead' : 'lightblue', 'tenlocalrepair' : 'lightblue'}
|
||||
for cat in dct:
|
||||
(x, y) = dct[cat]
|
||||
x=[int(2**int(v)) for v in x]
|
||||
#print(x, y)
|
||||
print(cat)
|
||||
axes.plot(x,y, color=colors[cat])
|
||||
axes.scatter(x,y, label=cat, marker=marker[cat], color=colors[cat])
|
||||
#plt.scatter(x, y, label=cat)
|
||||
#plt.plot(x, y)
|
||||
#axes.set_xticks([4, 6, 8, 10, 12, 14])
|
||||
index = 0
|
||||
if(args.invert): plt.title(titlesInvert[ReportPredictorType])
|
||||
else: plt.title(titles[ReportPredictorType])
|
||||
for branchPredName in sequencies:
|
||||
data = sequencies[branchPredName]
|
||||
(xdata, ydata) = zip(*data)
|
||||
if args.invert: ydata = [100 - x for x in ydata]
|
||||
axes.plot(xdata, ydata, color=colors[index])
|
||||
axes.scatter(xdata, ydata, label=branchPredName, color=colors[index], marker=markers[index])
|
||||
index = (index + 1) % len(markers)
|
||||
axes.legend(loc='upper left')
|
||||
axes.set_xscale("log")
|
||||
axes.set_ylabel('Prediction Accuracy')
|
||||
axes.set_xlabel('Entries')
|
||||
axes.set_xticks([64, 256, 1024, 4096, 16384, 65536])
|
||||
axes.set_xticklabels([64, 256, 1024, 4096, 16384, 65536])
|
||||
axes.set_xticks(xdata)
|
||||
axes.set_xticklabels(xdata)
|
||||
axes.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
|
||||
plt.show()
|
||||
|
||||
|
||||
else:
|
||||
# steps 1 and 2
|
||||
benchmarks = ProcessFile(sys.argv[1])
|
||||
print(benchmarks[0])
|
||||
ComputeAll(benchmarks)
|
||||
ComputeGeometricAverage(benchmarks)
|
||||
# 3 process into useful data
|
||||
# cache hit rates
|
||||
# cache fill time
|
||||
# branch predictor status
|
||||
# hazard counts
|
||||
# CPI
|
||||
# instruction distribution
|
||||
for benchmark in benchmarks:
|
||||
printStats(benchmark)
|
||||
# if(not args.summary):
|
||||
# size = len(benchmarkDict)
|
||||
# sizeSqrt = math.sqrt(size)
|
||||
# isSquare = math.isclose(sizeSqrt, round(sizeSqrt))
|
||||
# numCol = math.floor(sizeSqrt)
|
||||
# numRow = numCol + (0 if isSquare else 1)
|
||||
# index = 1
|
||||
# fig = plt.figure()
|
||||
# for benchmarkName in benchmarkDict:
|
||||
# currBenchmark = benchmarkDict[benchmarkName]
|
||||
# (names, typs, sizes, values) = FormatToPlot(currBenchmark)
|
||||
# #axes.plot(numRow, numCol, index)
|
||||
# ax = fig.add_subplot(numRow, numCol, index)
|
||||
# ax.bar(names, values)
|
||||
# ax.title.set_text(benchmarkName)
|
||||
# #plt.ylabel('BR Dir Miss Rate (%)')
|
||||
# #plt.xlabel('Predictor')
|
||||
# index += 1
|
||||
|
||||
if(not args.summary):
|
||||
size = len(benchmarkDict)
|
||||
sizeSqrt = math.sqrt(size)
|
||||
isSquare = math.isclose(sizeSqrt, round(sizeSqrt))
|
||||
numCol = math.floor(sizeSqrt)
|
||||
numRow = numCol + (0 if isSquare else 1)
|
||||
index = 1
|
||||
BenchPerRow = 7
|
||||
|
||||
xlabelList = []
|
||||
seriesDict = {}
|
||||
|
||||
for benchmarkName in benchmarkDict:
|
||||
currBenchmark = benchmarkDict[benchmarkName]
|
||||
xlabelList.append(benchmarkName)
|
||||
for (name, typ, size, value) in currBenchmark:
|
||||
if(name not in seriesDict):
|
||||
seriesDict[name] = [value]
|
||||
else:
|
||||
seriesDict[name].append(value)
|
||||
if(index >= BenchPerRow): break
|
||||
index += 1
|
||||
|
||||
xlabelListBig = []
|
||||
seriesDictBig = {}
|
||||
for benchmarkName in benchmarkDict:
|
||||
currBenchmark = benchmarkDict[benchmarkName]
|
||||
xlabelListBig.append(benchmarkName)
|
||||
for (name, typ, size, value) in currBenchmark:
|
||||
if(name not in seriesDictBig):
|
||||
seriesDictBig[name] = [value]
|
||||
else:
|
||||
seriesDictBig[name].append(value)
|
||||
|
||||
#The next step will be to split the benchmarkDict into length BenchPerRow pieces then repeat the following code
|
||||
# on each piece.
|
||||
for row in range(0, math.ceil(39 / BenchPerRow)):
|
||||
(xlabelListTrunk, seriesDictTrunk) = SelectPartition(xlabelListBig, seriesDictBig, row, BenchPerRow)
|
||||
FileName = 'barSegment%d.png' % row
|
||||
groupLen = len(xlabelListTrunk)
|
||||
BarGraph(seriesDictTrunk, xlabelListTrunk, groupLen, FileName)
|
||||
|
||||
|
||||
# main
|
||||
parser = argparse.ArgumentParser(description='Parses performance counters from a Questa Sim trace to produce a graph or graphs.')
|
||||
|
||||
# parse program arguments
|
||||
metric = parser.add_mutually_exclusive_group()
|
||||
metric.add_argument('-r', '--ras', action='store_const', help='Plot return address stack (RAS) performance.', default=False, const=True)
|
||||
metric.add_argument('-d', '--direction', action='store_const', help='Plot direction prediction (2-bit, Gshare, local, etc) performance.', default=False, const=True)
|
||||
metric.add_argument('-t', '--target', action='store_const', help='Plot branch target buffer (BTB) performance.', default=False, const=True)
|
||||
metric.add_argument('-c', '--iclass', action='store_const', help='Plot instruction classification performance.', default=False, const=True)
|
||||
|
||||
parser.add_argument('-s', '--summary', action='store_const', help='Show only the geometric average for all benchmarks.', 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('-i', '--invert', action='store_const', help='Invert metric. Example Branch miss prediction becomes prediction accuracy. 100 - miss rate', default=False, const=True)
|
||||
|
||||
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('--table', action='store_const', help='Display in text format only.', default=False, const=True)
|
||||
displayMode.add_argument('--gui', action='store_const', help='Display in text format only.', default=False, const=True)
|
||||
displayMode.add_argument('--debug', action='store_const', help='Display in text format only.', default=False, const=True)
|
||||
parser.add_argument('sources', nargs=1)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Figure what we are reporting
|
||||
ReportPredictorType = 'BDMR' # default
|
||||
if(args.ras): ReportPredictorType = 'RASMPR'
|
||||
if(args.target): ReportPredictorType = 'BTMR'
|
||||
if(args.iclass): ReportPredictorType = 'ClassMPR'
|
||||
|
||||
# Figure how we are displaying the data
|
||||
ReportMode = 'gui' # default
|
||||
if(args.text): ReportMode = 'text'
|
||||
if(args.table): ReportMode = 'table'
|
||||
if(args.debug): ReportMode = 'debug'
|
||||
|
||||
# read the questa sim list file.
|
||||
# row, col format. each row is a questa sim run with performance counters and a particular
|
||||
# branch predictor type and size. size can be multiple parameters for more complex predictors like
|
||||
# local history and tage.
|
||||
# <file> <type> <size>
|
||||
predictorLogs = ParseBranchListFile(args.sources[0]) # digests the traces
|
||||
performanceCounterList = BuildDataBase(predictorLogs) # builds a database of performance counters by trace and then by benchmark
|
||||
benchmarkFirstList = ReorderDataBase(performanceCounterList) # reorder first by benchmark then trace
|
||||
benchmarkDict = ExtractSelectedData(benchmarkFirstList) # filters to just the desired performance counter metric
|
||||
|
||||
if(args.reference): benchmarkDict['Mean'].extend(RefData)
|
||||
#print(benchmarkDict['Mean'])
|
||||
#print(benchmarkDict['aha-mont64Speed'])
|
||||
#print(benchmarkDict)
|
||||
|
||||
# table format
|
||||
if(ReportMode == 'table'):
|
||||
ReportAsTable(benchmarkDict)
|
||||
|
||||
if(ReportMode == 'text'):
|
||||
ReportAsText(benchmarkDict)
|
||||
|
||||
if(ReportMode == 'gui'):
|
||||
ReportAsGraph(benchmarkDict, args.bar)
|
||||
|
||||
# *** this is only needed of -b (no -s)
|
||||
|
||||
# debug
|
||||
#config0 = performanceCounterList[0][0]
|
||||
#data0 = performanceCounterList[0][1]
|
||||
#bench0 = data0[0]
|
||||
#bench0name = bench0[0]
|
||||
#bench0data = bench0[2]
|
||||
#bench0BrCount = bench0data['Br Count']
|
||||
#bench1 = data0[1]
|
||||
|
||||
#print(data0)
|
||||
#print(bench0)
|
||||
#print(bench1)
|
||||
|
||||
#print(bench0name)
|
||||
#print(bench0BrCount)
|
||||
|
||||
470
bin/parseTest.py
470
bin/parseTest.py
@ -1,470 +0,0 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
###########################################
|
||||
## Written: Rose Thompson ross1728@gmail.com
|
||||
## Created: 20 September 2023
|
||||
## Modified:
|
||||
##
|
||||
## Purpose: Parses the performance counters from a modelsim trace.
|
||||
##
|
||||
## A component of the CORE-V-WALLY configurable RISC-V project.
|
||||
##
|
||||
## Copyright (C) 2021-23 Harvey Mudd College & Oklahoma State University
|
||||
##
|
||||
## SPDX-License-Identifier: Apache-2.0 WITH SHL-2.1
|
||||
##
|
||||
## Licensed under the Solderpad Hardware License v 2.1 (the “License”); you may not use this file
|
||||
## except in compliance with the License, or, at your option, the Apache License version 2.0. You
|
||||
## may obtain a copy of the License at
|
||||
##
|
||||
## https:##solderpad.org/licenses/SHL-2.1/
|
||||
##
|
||||
## Unless required by applicable law or agreed to in writing, any work distributed under the
|
||||
## License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
|
||||
## either express or implied. See the License for the specific language governing permissions
|
||||
## and limitations under the License.
|
||||
################################################################################################
|
||||
|
||||
import os
|
||||
import sys
|
||||
import matplotlib.pyplot as plt
|
||||
import math
|
||||
import numpy as np
|
||||
import argparse
|
||||
|
||||
RefData = [('twobitCModel6', 'twobitCModel', 64, 9.65280765420711), ('twobitCModel8', 'twobitCModel', 256, 8.75120245829945), ('twobitCModel10', 'twobitCModel', 1024, 8.1318382397263),
|
||||
('twobitCModel12', 'twobitCModel', 4096, 7.53026646633342), ('twobitCModel14', 'twobitCModel', 16384, 6.07679338544009), ('twobitCModel16', 'twobitCModel', 65536, 6.07679338544009),
|
||||
('gshareCModel6', 'gshareCModel', 64, 10.6602835418646), ('gshareCModel8', 'gshareCModel', 256, 8.38384710559667), ('gshareCModel10', 'gshareCModel', 1024, 6.36847432155534),
|
||||
('gshareCModel12', 'gshareCModel', 4096, 3.91108491151983), ('gshareCModel14', 'gshareCModel', 16384, 2.83926519215395), ('gshareCModel16', 'gshareCModel', 65536, .60213659066941)]
|
||||
|
||||
def ParseBranchListFile(path):
|
||||
'''Take the path to the list of Questa Sim log files containing the performance counters outputs. File
|
||||
is formated in row columns. Each row is a trace with the file, branch predictor type, and the parameters.
|
||||
parameters can be any number and depend on the predictor type. Returns a list of lists.'''
|
||||
lst = []
|
||||
BranchList = open(path, 'r')
|
||||
for line in BranchList:
|
||||
tokens = line.split()
|
||||
predictorLog = os.path.dirname(path) + '/' + tokens[0]
|
||||
predictorType = tokens[1]
|
||||
predictorParams = tokens[2::]
|
||||
lst.append([predictorLog, predictorType, predictorParams])
|
||||
#print(predictorLog, predictorType, predictorParams)
|
||||
return lst
|
||||
|
||||
def ProcessFile(fileName):
|
||||
'''Extract preformance counters from a modelsim log. Outputs a list of tuples for each test/benchmark.
|
||||
The tuple contains the test name, optimization characteristics, and dictionary of performance counters.'''
|
||||
# 1 find lines with Read memfile and extract test name
|
||||
# 2 parse counters into a list of (name, value) tuples (dictionary maybe?)
|
||||
benchmarks = []
|
||||
transcript = open(fileName, 'r')
|
||||
HPMClist = { }
|
||||
testName = ''
|
||||
for line in transcript.readlines():
|
||||
lineToken = line.split()
|
||||
if(len(lineToken) > 3 and lineToken[1] == 'Read' and lineToken[2] == 'memfile'):
|
||||
opt = lineToken[3].split('/')[-4]
|
||||
testName = lineToken[3].split('/')[-1].split('.')[0]
|
||||
HPMClist = { }
|
||||
elif(len(lineToken) > 4 and lineToken[1][0:3] == 'Cnt'):
|
||||
countToken = line.split('=')[1].split()
|
||||
value = int(countToken[0]) if countToken[0] != 'x' else 0
|
||||
name = ' '.join(countToken[1:])
|
||||
HPMClist[name] = value
|
||||
elif ('is done' in line):
|
||||
benchmarks.append((testName, opt, HPMClist))
|
||||
return benchmarks
|
||||
|
||||
|
||||
def ComputeStats(benchmarks):
|
||||
for benchmark in benchmarks:
|
||||
(nameString, opt, dataDict) = benchmark
|
||||
dataDict['CPI'] = 1.0 * int(dataDict['Mcycle']) / int(dataDict['InstRet'])
|
||||
dataDict['BDMR'] = 100.0 * int(dataDict['BP Dir Wrong']) / int(dataDict['Br Count'])
|
||||
dataDict['BTMR'] = 100.0 * int(dataDict['BP Target Wrong']) / (int(dataDict['Br Count']) + int(dataDict['Jump Not Return']))
|
||||
dataDict['RASMPR'] = 100.0 * int(dataDict['RAS Wrong']) / int(dataDict['Return'])
|
||||
dataDict['ClassMPR'] = 100.0 * int(dataDict['Instr Class Wrong']) / int(dataDict['InstRet'])
|
||||
dataDict['ICacheMR'] = 100.0 * int(dataDict['I Cache Miss']) / int(dataDict['I Cache Access'])
|
||||
|
||||
cycles = int(dataDict['I Cache Miss'])
|
||||
if(cycles == 0): ICacheMR = 0
|
||||
else: ICacheMR = 100.0 * int(dataDict['I Cache Cycles']) / cycles
|
||||
dataDict['ICacheMT'] = ICacheMR
|
||||
|
||||
dataDict['DCacheMR'] = 100.0 * int(dataDict['D Cache Miss']) / int(dataDict['D Cache Access'])
|
||||
|
||||
(nameString, opt, dataDict) = benchmark
|
||||
cycles = int(dataDict['D Cache Miss'])
|
||||
if(cycles == 0): DCacheMR = 0
|
||||
else: DCacheMR = 100.0 * int(dataDict['D Cache Cycles']) / cycles
|
||||
dataDict['DCacheMT'] = DCacheMR
|
||||
|
||||
|
||||
def ComputeGeometricAverage(benchmarks):
|
||||
fields = ['BDMR', 'BTMR', 'RASMPR', 'ClassMPR', 'ICacheMR', 'DCacheMR', 'CPI', 'ICacheMT', 'DCacheMT']
|
||||
AllAve = {}
|
||||
for field in fields:
|
||||
Product = 1
|
||||
index = 0
|
||||
for (testName, opt, HPMCList) in benchmarks:
|
||||
#print(HPMCList)
|
||||
value = HPMCList[field]
|
||||
if(value != 0): Product *= value # if that value is 0 exclude from mean because it destories the geo mean
|
||||
index += 1
|
||||
AllAve[field] = Product ** (1.0/index)
|
||||
benchmarks.append(('Mean', '', AllAve))
|
||||
|
||||
def GenerateName(predictorType, predictorParams):
|
||||
if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class' or predictorType == 'ras'):
|
||||
return predictorType + predictorParams[0]
|
||||
elif(predictorParams == 'local'):
|
||||
return predictorType + predictorParams[0] + '_' + predictorParams[1]
|
||||
else:
|
||||
print(f'Error unsupported predictor type {predictorType}')
|
||||
sys.exit(-1)
|
||||
|
||||
def ComputePredNumEntries(predictorType, predictorParams):
|
||||
if(predictorType == 'gshare' or predictorType == 'twobit' or predictorType == 'btb' or predictorType == 'class'):
|
||||
return 2**int(predictorParams[0])
|
||||
elif(predictorType == 'ras'):
|
||||
return int(predictorParams[0])
|
||||
elif(predictorParams == 'local'):
|
||||
return 2**int(predictorParams[0]) * int(predictorParams[1]) + 2**int(predictorParams[1])
|
||||
else:
|
||||
print(f'Error unsupported predictor type {predictorType}')
|
||||
sys.exit(-1)
|
||||
|
||||
def BuildDataBase(predictorLogs):
|
||||
# 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.
|
||||
# list
|
||||
# branch predictor configuration 0 (tuple)
|
||||
# benchmark name
|
||||
# compiler optimization
|
||||
# data (dictionary)
|
||||
# dictionary of performance counters
|
||||
# branch predictor configuration 1 (tuple)
|
||||
# benchmark name (dictionary)
|
||||
# compiler optimization
|
||||
# data
|
||||
# dictionary of performance counters
|
||||
# ...
|
||||
performanceCounterList = []
|
||||
for trace in predictorLogs:
|
||||
predictorLog = trace[0]
|
||||
predictorType = trace[1]
|
||||
predictorParams = trace[2]
|
||||
# Extract the performance counter data
|
||||
performanceCounters = ProcessFile(predictorLog)
|
||||
ComputeStats(performanceCounters)
|
||||
ComputeGeometricAverage(performanceCounters)
|
||||
#print(performanceCounters)
|
||||
performanceCounterList.append([GenerateName(predictorType, predictorParams), predictorType, performanceCounters, ComputePredNumEntries(predictorType, predictorParams)])
|
||||
return performanceCounterList
|
||||
|
||||
def ReorderDataBase(performanceCounterList):
|
||||
# Reorder the data so the benchmark name comes first, then the branch predictor configuration
|
||||
benchmarkFirstList = []
|
||||
for (predictorName, predictorPrefixName, benchmarks, entries) in performanceCounterList:
|
||||
for benchmark in benchmarks:
|
||||
(nameString, opt, dataDict) = benchmark
|
||||
benchmarkFirstList.append((nameString, opt, predictorName, predictorPrefixName, entries, dataDict))
|
||||
return benchmarkFirstList
|
||||
|
||||
def ExtractSelectedData(benchmarkFirstList):
|
||||
# now extract all branch prediction direction miss rates for each
|
||||
# namestring + opt, config
|
||||
benchmarkDict = { }
|
||||
for benchmark in benchmarkFirstList:
|
||||
(name, opt, config, prefixName, entries, dataDict) = benchmark
|
||||
if opt == 'bd_speedopt_speed': NewName = name+'Sp'
|
||||
elif opt == 'bd_sizeopt_speed': NewName = name+'Sz'
|
||||
else: NewName = name
|
||||
#print(NewName)
|
||||
#NewName = name+'_'+opt
|
||||
if NewName in benchmarkDict:
|
||||
benchmarkDict[NewName].append((config, prefixName, entries, dataDict[ReportPredictorType]))
|
||||
else:
|
||||
benchmarkDict[NewName] = [(config, prefixName, entries, dataDict[ReportPredictorType])]
|
||||
return benchmarkDict
|
||||
|
||||
def ReportAsTable(benchmarkDict):
|
||||
refLine = benchmarkDict['Mean']
|
||||
FirstLine = []
|
||||
SecondLine = []
|
||||
for (name, typ, size, val) in refLine:
|
||||
FirstLine.append(name)
|
||||
SecondLine.append(size)
|
||||
|
||||
sys.stdout.write('benchmark\t\t')
|
||||
for name in FirstLine:
|
||||
if(len(name) < 8): sys.stdout.write('%s\t\t' % name)
|
||||
else: sys.stdout.write('%s\t' % name)
|
||||
sys.stdout.write('\n')
|
||||
sys.stdout.write('size\t\t\t')
|
||||
for size in SecondLine:
|
||||
if(len(str(size)) < 8): sys.stdout.write('%d\t\t' % size)
|
||||
else: sys.stdout.write('%d\t' % size)
|
||||
sys.stdout.write('\n')
|
||||
|
||||
if(args.summary):
|
||||
sys.stdout.write('Mean\t\t\t')
|
||||
for (name, typ, size, val) in refLine:
|
||||
sys.stdout.write('%0.2f\t\t' % (val if not args.invert else 100 - val))
|
||||
sys.stdout.write('\n')
|
||||
|
||||
if(not args.summary):
|
||||
for benchmark in benchmarkDict:
|
||||
length = len(benchmark)
|
||||
if(length < 8): sys.stdout.write('%s\t\t\t' % benchmark)
|
||||
elif(length < 16): sys.stdout.write('%s\t\t' % benchmark)
|
||||
else: sys.stdout.write('%s\t' % benchmark)
|
||||
for (name, typ, size, val) in benchmarkDict[benchmark]:
|
||||
sys.stdout.write('%0.2f\t\t' % (val if not args.invert else 100 -val))
|
||||
sys.stdout.write('\n')
|
||||
|
||||
def ReportAsText(benchmarkDict):
|
||||
if(args.summary):
|
||||
mean = benchmarkDict['Mean']
|
||||
print('Mean')
|
||||
for (name, typ, size, val) in mean:
|
||||
sys.stdout.write('%s %s %0.2f\n' % (name, size, val if not args.invert else 100 - val))
|
||||
|
||||
if(not args.summary):
|
||||
for benchmark in benchmarkDict:
|
||||
print(benchmark)
|
||||
for (name, type, size, val) in benchmarkDict[benchmark]:
|
||||
sys.stdout.write('%s %s %0.2f\n' % (name, size, val if not args.invert else 100 - val))
|
||||
|
||||
def Inversion(lst):
|
||||
return [x if not args.invert else 100 - x for x in lst]
|
||||
|
||||
def BarGraph(seriesDict, xlabelList, BenchPerRow, FileName):
|
||||
index = 0
|
||||
NumberInGroup = len(seriesDict)
|
||||
# Figure out width of bars. NumberInGroup bars + want 2 bar space
|
||||
# the space between groups is 1
|
||||
EffectiveNumInGroup = NumberInGroup + 2
|
||||
barWidth = 1 / EffectiveNumInGroup
|
||||
fig = plt.subplots(figsize = (EffectiveNumInGroup*BenchPerRow/8, 4))
|
||||
colors = ['blue', 'blue', 'blue', 'blue', 'blue', 'blue', 'black', 'black', 'black', 'black', 'black', 'black']
|
||||
for name in seriesDict:
|
||||
xpos = np.arange(BenchPerRow)
|
||||
xpos = [x + index*barWidth for x in xpos]
|
||||
values = seriesDict[name]
|
||||
plt.bar(xpos, Inversion(values), width=barWidth, edgecolor='grey', label=name, color=colors[index%len(colors)])
|
||||
index += 1
|
||||
plt.xticks([r + barWidth*(NumberInGroup/2-0.5) for r in range(0, BenchPerRow)], xlabelList)
|
||||
plt.xlabel('Benchmark')
|
||||
if(not args.invert): plt.ylabel('Misprediction Rate (%)')
|
||||
else: plt.ylabel('Prediction Accuracy (%)')
|
||||
plt.legend(loc='upper left', ncol=2)
|
||||
plt.savefig(FileName)
|
||||
|
||||
def SelectPartition(xlabelListBig, seriesDictBig, group, BenchPerRow):
|
||||
seriesDictTrunk = {}
|
||||
for benchmarkName in seriesDictBig:
|
||||
lst = seriesDictBig[benchmarkName]
|
||||
seriesDictTrunk[benchmarkName] = lst[group*BenchPerRow:(group+1)*BenchPerRow]
|
||||
xlabelListTrunk = xlabelListBig[group*BenchPerRow:(group+1)*BenchPerRow]
|
||||
return(xlabelListTrunk, seriesDictTrunk)
|
||||
|
||||
|
||||
def ReportAsGraph(benchmarkDict, bar):
|
||||
def FormatToPlot(currBenchmark):
|
||||
names = []
|
||||
sizes = []
|
||||
values = []
|
||||
typs = []
|
||||
for config in currBenchmark:
|
||||
names.append(config[0])
|
||||
sizes.append(config[1])
|
||||
values.append(config[2])
|
||||
typs.append(config[3])
|
||||
return (names, sizes, values, typs)
|
||||
titlesInvert = {'BDMR' : 'Branch Direction Accuracy',
|
||||
'BTMR' : 'Branch Target Accuracy',
|
||||
'RASMPR': 'RAS Accuracy',
|
||||
'ClassMPR': 'Class Prediction Accuracy'}
|
||||
titles = {'BDMR' : 'Branch Direction Misprediction',
|
||||
'BTMR' : 'Branch Target Misprediction',
|
||||
'RASMPR': 'RAS Misprediction',
|
||||
'ClassMPR': 'Class Misprediction'}
|
||||
if(args.summary):
|
||||
markers = ['x', '.', '+', '*', '^', 'o', ',', 's']
|
||||
colors = ['blue', 'black', 'gray', 'dodgerblue', 'lightsteelblue', 'turquoise', 'black', 'blue']
|
||||
temp = benchmarkDict['Mean']
|
||||
|
||||
# the benchmarkDict['Mean'] contains sequencies of results for multiple
|
||||
# branch predictors with various parameterizations
|
||||
# group the parameterizations by the common typ.
|
||||
sequencies = {}
|
||||
for (name, typ, size, value) in benchmarkDict['Mean']:
|
||||
if not typ in sequencies:
|
||||
sequencies[typ] = [(size, value)]
|
||||
else:
|
||||
sequencies[typ].append((size,value))
|
||||
# then graph the common typ as a single line+scatter plot
|
||||
# finally repeat for all typs of branch predictors and overlay
|
||||
fig, axes = plt.subplots()
|
||||
index = 0
|
||||
if(args.invert): plt.title(titlesInvert[ReportPredictorType])
|
||||
else: plt.title(titles[ReportPredictorType])
|
||||
for branchPredName in sequencies:
|
||||
data = sequencies[branchPredName]
|
||||
(xdata, ydata) = zip(*data)
|
||||
if args.invert: ydata = [100 - x for x in ydata]
|
||||
axes.plot(xdata, ydata, color=colors[index])
|
||||
axes.scatter(xdata, ydata, label=branchPredName, color=colors[index], marker=markers[index])
|
||||
index = (index + 1) % len(markers)
|
||||
axes.legend(loc='upper left')
|
||||
axes.set_xscale("log")
|
||||
axes.set_ylabel('Prediction Accuracy')
|
||||
axes.set_xlabel('Entries')
|
||||
axes.set_xticks(xdata)
|
||||
axes.set_xticklabels(xdata)
|
||||
axes.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
|
||||
plt.show()
|
||||
|
||||
|
||||
# if(not args.summary):
|
||||
# size = len(benchmarkDict)
|
||||
# sizeSqrt = math.sqrt(size)
|
||||
# isSquare = math.isclose(sizeSqrt, round(sizeSqrt))
|
||||
# numCol = math.floor(sizeSqrt)
|
||||
# numRow = numCol + (0 if isSquare else 1)
|
||||
# index = 1
|
||||
# fig = plt.figure()
|
||||
# for benchmarkName in benchmarkDict:
|
||||
# currBenchmark = benchmarkDict[benchmarkName]
|
||||
# (names, typs, sizes, values) = FormatToPlot(currBenchmark)
|
||||
# #axes.plot(numRow, numCol, index)
|
||||
# ax = fig.add_subplot(numRow, numCol, index)
|
||||
# ax.bar(names, values)
|
||||
# ax.title.set_text(benchmarkName)
|
||||
# #plt.ylabel('BR Dir Miss Rate (%)')
|
||||
# #plt.xlabel('Predictor')
|
||||
# index += 1
|
||||
|
||||
if(not args.summary):
|
||||
size = len(benchmarkDict)
|
||||
sizeSqrt = math.sqrt(size)
|
||||
isSquare = math.isclose(sizeSqrt, round(sizeSqrt))
|
||||
numCol = math.floor(sizeSqrt)
|
||||
numRow = numCol + (0 if isSquare else 1)
|
||||
index = 1
|
||||
BenchPerRow = 7
|
||||
|
||||
xlabelList = []
|
||||
seriesDict = {}
|
||||
|
||||
for benchmarkName in benchmarkDict:
|
||||
currBenchmark = benchmarkDict[benchmarkName]
|
||||
xlabelList.append(benchmarkName)
|
||||
for (name, typ, size, value) in currBenchmark:
|
||||
if(name not in seriesDict):
|
||||
seriesDict[name] = [value]
|
||||
else:
|
||||
seriesDict[name].append(value)
|
||||
if(index >= BenchPerRow): break
|
||||
index += 1
|
||||
|
||||
xlabelListBig = []
|
||||
seriesDictBig = {}
|
||||
for benchmarkName in benchmarkDict:
|
||||
currBenchmark = benchmarkDict[benchmarkName]
|
||||
xlabelListBig.append(benchmarkName)
|
||||
for (name, typ, size, value) in currBenchmark:
|
||||
if(name not in seriesDictBig):
|
||||
seriesDictBig[name] = [value]
|
||||
else:
|
||||
seriesDictBig[name].append(value)
|
||||
|
||||
#The next step will be to split the benchmarkDict into length BenchPerRow pieces then repeat the following code
|
||||
# on each piece.
|
||||
for row in range(0, math.ceil(39 / BenchPerRow)):
|
||||
(xlabelListTrunk, seriesDictTrunk) = SelectPartition(xlabelListBig, seriesDictBig, row, BenchPerRow)
|
||||
FileName = 'barSegment%d.png' % row
|
||||
groupLen = len(xlabelListTrunk)
|
||||
BarGraph(seriesDictTrunk, xlabelListTrunk, groupLen, FileName)
|
||||
|
||||
|
||||
# main
|
||||
parser = argparse.ArgumentParser(description='Parses performance counters from a Questa Sim trace to produce a graph or graphs.')
|
||||
|
||||
# parse program arguments
|
||||
metric = parser.add_mutually_exclusive_group()
|
||||
metric.add_argument('-r', '--ras', action='store_const', help='Plot return address stack (RAS) performance.', default=False, const=True)
|
||||
metric.add_argument('-d', '--direction', action='store_const', help='Plot direction prediction (2-bit, Gshare, local, etc) performance.', default=False, const=True)
|
||||
metric.add_argument('-t', '--target', action='store_const', help='Plot branch target buffer (BTB) performance.', default=False, const=True)
|
||||
metric.add_argument('-c', '--iclass', action='store_const', help='Plot instruction classification performance.', default=False, const=True)
|
||||
|
||||
parser.add_argument('-s', '--summary', action='store_const', help='Show only the geometric average for all benchmarks.', 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('-i', '--invert', action='store_const', help='Invert metric. Example Branch miss prediction becomes prediction accuracy. 100 - miss rate', default=False, const=True)
|
||||
|
||||
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('--table', action='store_const', help='Display in text format only.', default=False, const=True)
|
||||
displayMode.add_argument('--gui', action='store_const', help='Display in text format only.', default=False, const=True)
|
||||
displayMode.add_argument('--debug', action='store_const', help='Display in text format only.', default=False, const=True)
|
||||
parser.add_argument('sources', nargs=1)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Figure what we are reporting
|
||||
ReportPredictorType = 'BDMR' # default
|
||||
if(args.ras): ReportPredictorType = 'RASMPR'
|
||||
if(args.target): ReportPredictorType = 'BTMR'
|
||||
if(args.iclass): ReportPredictorType = 'ClassMPR'
|
||||
|
||||
# Figure how we are displaying the data
|
||||
ReportMode = 'gui' # default
|
||||
if(args.text): ReportMode = 'text'
|
||||
if(args.table): ReportMode = 'table'
|
||||
if(args.debug): ReportMode = 'debug'
|
||||
|
||||
# read the questa sim list file.
|
||||
# row, col format. each row is a questa sim run with performance counters and a particular
|
||||
# branch predictor type and size. size can be multiple parameters for more complex predictors like
|
||||
# local history and tage.
|
||||
# <file> <type> <size>
|
||||
predictorLogs = ParseBranchListFile(args.sources[0]) # digests the traces
|
||||
performanceCounterList = BuildDataBase(predictorLogs) # builds a database of performance counters by trace and then by benchmark
|
||||
benchmarkFirstList = ReorderDataBase(performanceCounterList) # reorder first by benchmark then trace
|
||||
benchmarkDict = ExtractSelectedData(benchmarkFirstList) # filters to just the desired performance counter metric
|
||||
|
||||
if(args.reference): benchmarkDict['Mean'].extend(RefData)
|
||||
#print(benchmarkDict['Mean'])
|
||||
#print(benchmarkDict['aha-mont64Speed'])
|
||||
#print(benchmarkDict)
|
||||
|
||||
# table format
|
||||
if(ReportMode == 'table'):
|
||||
ReportAsTable(benchmarkDict)
|
||||
|
||||
if(ReportMode == 'text'):
|
||||
ReportAsText(benchmarkDict)
|
||||
|
||||
if(ReportMode == 'gui'):
|
||||
ReportAsGraph(benchmarkDict, args.bar)
|
||||
|
||||
# *** this is only needed of -b (no -s)
|
||||
|
||||
# debug
|
||||
#config0 = performanceCounterList[0][0]
|
||||
#data0 = performanceCounterList[0][1]
|
||||
#bench0 = data0[0]
|
||||
#bench0name = bench0[0]
|
||||
#bench0data = bench0[2]
|
||||
#bench0BrCount = bench0data['Br Count']
|
||||
#bench1 = data0[1]
|
||||
|
||||
#print(data0)
|
||||
#print(bench0)
|
||||
#print(bench1)
|
||||
|
||||
#print(bench0name)
|
||||
#print(bench0BrCount)
|
||||
Loading…
Reference in New Issue
Block a user