| 1 | import os
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| 2 | import sys
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| 3 | import time
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| 4 | import itertools
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| 5 | import matplotlib.pyplot as plt
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| 6 | import matplotlib.ticker as ticks
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| 7 | import math
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| 8 | from scipy import stats as st
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| 9 | import numpy as np
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| 10 | from enum import Enum
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| 11 | from statistics import median
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| 12 |
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| 13 | import matplotlib
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| 14 | matplotlib.use("pgf")
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| 15 | matplotlib.rcParams.update({
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| 16 | "pgf.texsystem": "pdflatex",
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| 17 | 'font.family': 'serif',
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| 18 | 'text.usetex': True,
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| 19 | 'pgf.rcfonts': False,
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| 20 | 'font.size': 16
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| 21 | })
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| 22 | marker = itertools.cycle(('o', 's', 'D', 'x', 'p', '^', 'h', '*', 'v' ))
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| 23 |
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| 24 | def sci_format(x, pos):
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| 25 | return '{:.1e}'.format(x).replace('+0', '')
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| 26 |
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| 27 | readfile = open(sys.argv[1], "r")
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| 28 |
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| 29 | machineName = ""
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| 30 |
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| 31 | if len(sys.argv) > 2:
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| 32 | machineName = sys.argv[2]
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| 33 |
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| 34 | # first line has num times per experiment
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| 35 | line = readfile.readline()
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| 36 | numTimes = int(line)
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| 37 |
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| 38 | # second line has processor args
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| 39 | line = readfile.readline()
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| 40 | procs = []
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| 41 | for val in line.split():
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| 42 | procs.append(int(val))
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| 43 |
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| 44 | # 3rd line has number of variants
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| 45 | line = readfile.readline()
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| 46 | names = line.split()
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| 47 | numVariants = len(names)
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| 48 |
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| 49 | lines = (line.rstrip() for line in readfile) # All lines including the blank ones
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| 50 | lines = (line for line in lines if line) # Non-blank lines
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| 51 |
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| 52 | class Bench(Enum):
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| 53 | Unset = 0
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| 54 | Contend = 1
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| 55 | Zero = 2
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| 56 | Barrier = 3
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| 57 | Churn = 4
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| 58 | Daisy_Chain = 5
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| 59 | Hot_Potato = 6
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| 60 | Pub_Sub = 7
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| 61 |
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| 62 | nameSet = False
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| 63 | currBench = Bench.Unset # default val
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| 64 | count = 0
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| 65 | procCount = 0
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| 66 | currVariant = 0
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| 67 | experiment_duration = 10.0
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| 68 | name = ""
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| 69 | title = ""
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| 70 | var_name = ""
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| 71 | sendData = [0.0 for j in range(numVariants)]
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| 72 | data = [[0.0 for i in range(len(procs))] for j in range(numVariants)]
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| 73 | bars = [[[0.0 for i in range(len(procs))],[0.0 for k in range(len(procs))]] for j in range(numVariants)]
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| 74 | tempData = [0.0 for i in range(numTimes)]
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| 75 | for idx, line in enumerate(lines):
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| 76 | # print(line)
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| 77 |
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| 78 | if currBench == Bench.Unset:
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| 79 | if line == "contend:":
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| 80 | name = "Channel_Contention"
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| 81 | title = "Channel Contention"
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| 82 | currBench = Bench.Contend
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| 83 | elif line == "zero:":
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| 84 | name = "Zero"
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| 85 | currBench = Bench.Zero
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| 86 | elif line == "barrier:":
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| 87 | name = "Barrier"
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| 88 | currBench = Bench.Barrier
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| 89 | elif line == "churn:":
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| 90 | name = "Churn"
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| 91 | currBench = Bench.Churn
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| 92 | elif line == "daisy_chain:":
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| 93 | name = "Daisy_Chain"
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| 94 | currBench = Bench.Daisy_Chain
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| 95 | elif line == "hot_potato:":
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| 96 | name = "Hot_Potato"
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| 97 | currBench = Bench.Hot_Potato
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| 98 | elif line == "pub_sub:":
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| 99 | name = "Pub_Sub"
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| 100 | currBench = Bench.Pub_Sub
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| 101 | else:
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| 102 | print("Expected benchmark name")
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| 103 | print("Line: " + line)
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| 104 | sys.exit()
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| 105 | continue
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| 106 |
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| 107 | if line[0:5] == "cores":
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| 108 | continue
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| 109 |
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| 110 | if not nameSet:
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| 111 | nameSet = True
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| 112 | continue
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| 113 |
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| 114 | lineArr = line.split()
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| 115 | tempData[count] = float(lineArr[-1]) / experiment_duration
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| 116 | count += 1
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| 117 | if count == numTimes:
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| 118 | currMedian = median( tempData )
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| 119 | data[currVariant][procCount] = currMedian
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| 120 | lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData))
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| 121 | bars[currVariant][0][procCount] = currMedian - lower
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| 122 | bars[currVariant][1][procCount] = upper - currMedian
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| 123 | count = 0
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| 124 | procCount += 1
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| 125 |
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| 126 | if procCount == len(procs):
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| 127 | procCount = 0
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| 128 | nameSet = False
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| 129 | currVariant += 1
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| 130 |
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| 131 | if currVariant == numVariants:
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| 132 | fig, ax = plt.subplots(layout='constrained')
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| 133 | if title != "":
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| 134 | plt.title(title + " Benchmark")
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| 135 | title = ""
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| 136 | else:
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| 137 | plt.title(name + " Benchmark")
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| 138 | plt.ylabel("Throughput (channel operations per second)")
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| 139 | plt.xlabel("Cores")
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| 140 | ax.yaxis.set_major_formatter(ticks.FuncFormatter(sci_format))
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| 141 | for idx, arr in enumerate(data):
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| 142 | plt.errorbar( procs, arr, [bars[idx][0], bars[idx][1]], capsize=2, marker=next(marker) )
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| 143 | marker = itertools.cycle(('o', 's', 'D', 'x', 'p', '^', 'h', '*', 'v' ))
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| 144 | # plt.yscale("log")
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| 145 | # plt.ylim(1, None)
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| 146 | # ax.get_yaxis().set_major_formatter(ticks.ScalarFormatter())
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| 147 | # else:
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| 148 | # plt.ylim(0, None)
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| 149 | plt.xticks(procs)
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| 150 | ax.legend(names)
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| 151 | # fig.savefig("plots/" + machineName + name + ".png")
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| 152 | plt.savefig("plots/" + machineName + name + ".pgf")
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| 153 | fig.clf()
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| 154 |
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| 155 | # reset
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| 156 | currBench = Bench.Unset
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| 157 | currVariant = 0
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