[2f6a9391] | 1 | import os |
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| 2 | import sys |
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| 3 | import time |
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[4912520] | 4 | import itertools |
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[2f6a9391] | 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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[4912520] | 20 | 'font.size': 16 |
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[2f6a9391] | 21 | }) |
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[4912520] | 22 | marker = itertools.cycle(('o', 's', 'D', 'x', 'p', '^', 'h', '*', 'v' )) |
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[2f6a9391] | 23 | |
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| 24 | readfile = open(sys.argv[1], "r") |
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| 25 | |
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| 26 | machineName = "" |
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| 27 | |
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| 28 | if len(sys.argv) > 2: |
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| 29 | machineName = sys.argv[2] |
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| 30 | |
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| 31 | # first line has num times per experiment |
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| 32 | line = readfile.readline() |
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| 33 | numTimes = int(line) |
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| 34 | |
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| 35 | # second line has processor args |
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| 36 | line = readfile.readline() |
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| 37 | procs = [] |
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| 38 | for val in line.split(): |
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| 39 | procs.append(int(val)) |
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| 40 | |
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[d24b1985] | 41 | # 3rd line has number of variants |
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[2f6a9391] | 42 | line = readfile.readline() |
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| 43 | names = line.split() |
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| 44 | numVariants = len(names) |
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| 45 | |
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| 46 | lines = (line.rstrip() for line in readfile) # All lines including the blank ones |
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| 47 | lines = (line for line in lines if line) # Non-blank lines |
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| 48 | |
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[d24b1985] | 49 | class Bench(Enum): |
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| 50 | Unset = 0 |
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| 51 | Contend = 1 |
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| 52 | Zero = 2 |
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| 53 | Barrier = 3 |
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| 54 | Churn = 4 |
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| 55 | Daisy_Chain = 5 |
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| 56 | Hot_Potato = 6 |
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| 57 | Pub_Sub = 7 |
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| 58 | |
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[2f6a9391] | 59 | nameSet = False |
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[d24b1985] | 60 | currBench = Bench.Unset # default val |
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[2f6a9391] | 61 | count = 0 |
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| 62 | procCount = 0 |
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| 63 | currVariant = 0 |
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[d24b1985] | 64 | name = "" |
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[2f6a9391] | 65 | var_name = "" |
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| 66 | sendData = [0.0 for j in range(numVariants)] |
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| 67 | data = [[0.0 for i in range(len(procs))] for j in range(numVariants)] |
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| 68 | 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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| 69 | tempData = [0.0 for i in range(numTimes)] |
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| 70 | for idx, line in enumerate(lines): |
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| 71 | # print(line) |
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| 72 | |
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[d24b1985] | 73 | if currBench == Bench.Unset: |
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| 74 | if line == "contend:": |
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[6e1e2d0] | 75 | name = "Channel Contention" |
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[d24b1985] | 76 | currBench = Bench.Contend |
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| 77 | elif line == "zero:": |
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| 78 | name = "Zero" |
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| 79 | currBench = Bench.Zero |
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| 80 | elif line == "barrier:": |
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| 81 | name = "Barrier" |
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| 82 | currBench = Bench.Barrier |
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| 83 | elif line == "churn:": |
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| 84 | name = "Churn" |
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| 85 | currBench = Bench.Churn |
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| 86 | elif line == "daisy_chain:": |
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| 87 | name = "Daisy_Chain" |
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| 88 | currBench = Bench.Daisy_Chain |
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| 89 | elif line == "hot_potato:": |
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| 90 | name = "Hot_Potato" |
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| 91 | currBench = Bench.Hot_Potato |
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| 92 | elif line == "pub_sub:": |
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| 93 | name = "Pub_Sub" |
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| 94 | currBench = Bench.Pub_Sub |
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| 95 | else: |
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| 96 | print("Expected benchmark name") |
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| 97 | print("Line: " + line) |
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| 98 | sys.exit() |
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[2f6a9391] | 99 | continue |
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| 100 | |
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| 101 | if line[0:5] == "cores": |
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| 102 | continue |
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| 103 | |
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| 104 | if not nameSet: |
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| 105 | nameSet = True |
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| 106 | continue |
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| 107 | |
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| 108 | lineArr = line.split() |
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| 109 | tempData[count] = float(lineArr[-1]) |
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| 110 | count += 1 |
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| 111 | if count == numTimes: |
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| 112 | currMedian = median( tempData ) |
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| 113 | data[currVariant][procCount] = currMedian |
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| 114 | lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData)) |
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| 115 | bars[currVariant][0][procCount] = currMedian - lower |
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| 116 | bars[currVariant][1][procCount] = upper - currMedian |
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| 117 | count = 0 |
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| 118 | procCount += 1 |
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| 119 | |
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| 120 | if procCount == len(procs): |
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| 121 | procCount = 0 |
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| 122 | nameSet = False |
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| 123 | currVariant += 1 |
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| 124 | |
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| 125 | if currVariant == numVariants: |
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[1803d4d] | 126 | fig, ax = plt.subplots(layout='constrained') |
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[d24b1985] | 127 | plt.title(name + " Benchmark") |
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| 128 | plt.ylabel("Throughput (channel operations)") |
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[2f6a9391] | 129 | plt.xlabel("Cores") |
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| 130 | for idx, arr in enumerate(data): |
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[4912520] | 131 | plt.errorbar( procs, arr, [bars[idx][0], bars[idx][1]], capsize=2, marker=next(marker) ) |
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[d24b1985] | 132 | |
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[2f6a9391] | 133 | plt.yscale("log") |
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[d24b1985] | 134 | # plt.ylim(1, None) |
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| 135 | # ax.get_yaxis().set_major_formatter(ticks.ScalarFormatter()) |
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| 136 | # else: |
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| 137 | # plt.ylim(0, None) |
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[2f6a9391] | 138 | plt.xticks(procs) |
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| 139 | ax.legend(names) |
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[d24b1985] | 140 | # fig.savefig("plots/" + machineName + name + ".png") |
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| 141 | plt.savefig("plots/" + machineName + name + ".pgf") |
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[2f6a9391] | 142 | fig.clf() |
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| 143 | |
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| 144 | # reset |
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[d24b1985] | 145 | currBench = Bench.Unset |
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[2f6a9391] | 146 | currVariant = 0 |
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