[bb9b8f0] | 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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[bb9b8f0] | 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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[bb9b8f0] | 21 | }) |
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[4912520] | 22 | marker = itertools.cycle(('o', 's', 'D', 'x', 'p', '^', 'h', '*', 'v' )) |
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[bb9b8f0] | 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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| 41 | # 3rd line has num locks args |
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| 42 | line = readfile.readline() |
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| 43 | locks = [] |
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| 44 | for val in line.split(): |
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| 45 | locks.append(int(val)) |
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| 46 | |
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| 47 | # 4th line has number of variants |
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| 48 | line = readfile.readline() |
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| 49 | names = line.split() |
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| 50 | numVariants = len(names) |
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| 51 | |
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| 52 | lines = (line.rstrip() for line in readfile) # All lines including the blank ones |
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| 53 | lines = (line for line in lines if line) # Non-blank lines |
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| 54 | |
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| 55 | nameSet = False |
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| 56 | currLocks = -1 # default val |
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| 57 | count = 0 |
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| 58 | procCount = 0 |
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| 59 | currVariant = 0 |
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| 60 | name = "Aggregate Lock" |
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| 61 | var_name = "" |
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| 62 | sendData = [0.0 for j in range(numVariants)] |
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| 63 | data = [[0.0 for i in range(len(procs))] for j in range(numVariants)] |
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| 64 | 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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| 65 | tempData = [0.0 for i in range(numTimes)] |
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| 66 | for idx, line in enumerate(lines): |
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| 67 | # print(line) |
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| 68 | |
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| 69 | if currLocks == -1: |
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| 70 | lineArr = line.split() |
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| 71 | currLocks = lineArr[-1] |
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| 72 | continue |
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| 73 | |
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| 74 | if line[0:5] == "cores": |
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| 75 | continue |
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| 76 | |
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| 77 | if not nameSet: |
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| 78 | nameSet = True |
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| 79 | continue |
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| 80 | |
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| 81 | lineArr = line.split() |
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| 82 | tempData[count] = float(lineArr[-1]) |
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| 83 | count += 1 |
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| 84 | if count == numTimes: |
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| 85 | currMedian = median( tempData ) |
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| 86 | data[currVariant][procCount] = currMedian |
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| 87 | lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData)) |
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| 88 | bars[currVariant][0][procCount] = currMedian - lower |
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| 89 | bars[currVariant][1][procCount] = upper - currMedian |
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| 90 | count = 0 |
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| 91 | procCount += 1 |
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| 92 | |
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| 93 | if procCount == len(procs): |
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| 94 | procCount = 0 |
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| 95 | nameSet = False |
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| 96 | currVariant += 1 |
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| 97 | |
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| 98 | if currVariant == numVariants: |
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[5eb9327] | 99 | fig, ax = plt.subplots(layout='constrained') |
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[bb9b8f0] | 100 | plt.title(name + " Benchmark: " + str(currLocks) + " Locks") |
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| 101 | plt.ylabel("Throughput (entries)") |
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| 102 | plt.xlabel("Cores") |
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| 103 | for idx, arr in enumerate(data): |
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[4912520] | 104 | plt.errorbar( procs, arr, [bars[idx][0], bars[idx][1]], capsize=2, marker=next(marker) ) |
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[bb9b8f0] | 105 | plt.yscale("log") |
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| 106 | plt.xticks(procs) |
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| 107 | ax.legend(names) |
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[4eebbcc] | 108 | # fig.savefig("plots/" + machineName + "Aggregate_Lock_" + str(currLocks) + ".png") |
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| 109 | plt.savefig("plots/" + machineName + "Aggregate_Lock_" + str(currLocks) + ".pgf") |
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[bb9b8f0] | 110 | fig.clf() |
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| 111 | |
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| 112 | # reset |
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| 113 | currLocks = -1 |
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| 114 | currVariant = 0 |
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