| 1 | import os
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| 2 | import sys
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| 3 | import time
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| 4 | import matplotlib.pyplot as plt
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| 5 | import matplotlib.ticker as ticks
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| 6 | import math
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| 7 | from scipy import stats as st
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| 8 | import numpy as np
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| 9 | from enum import Enum
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| 10 | from statistics import median
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| 11 |
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| 12 | import matplotlib
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| 13 | matplotlib.use("pgf")
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| 14 | matplotlib.rcParams.update({
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| 15 | "pgf.texsystem": "pdflatex",
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| 16 | 'font.family': 'serif',
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| 17 | 'text.usetex': True,
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| 18 | 'pgf.rcfonts': False,
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| 19 | })
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| 20 |
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| 21 | readfile = open(sys.argv[1], "r")
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| 22 |
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| 23 | machineName = ""
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| 24 |
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| 25 | if len(sys.argv) > 2:
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| 26 | machineName = sys.argv[2]
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| 27 |
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| 28 | # first line has num times per experiment
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| 29 | line = readfile.readline()
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| 30 | numTimes = int(line)
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| 31 |
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| 32 | # second line has processor args
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| 33 | line = readfile.readline()
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| 34 | procs = []
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| 35 | for val in line.split():
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| 36 | procs.append(int(val))
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| 37 |
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| 38 | # 3rd line has num locks args
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| 39 | line = readfile.readline()
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| 40 | locks = []
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| 41 | for val in line.split():
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| 42 | locks.append(int(val))
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| 43 |
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| 44 | # 4th 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 | nameSet = False
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| 53 | currLocks = -1 # default val
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| 54 | count = 0
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| 55 | procCount = 0
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| 56 | currVariant = 0
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| 57 | name = "Aggregate Lock"
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| 58 | var_name = ""
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| 59 | sendData = [0.0 for j in range(numVariants)]
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| 60 | data = [[0.0 for i in range(len(procs))] for j in range(numVariants)]
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| 61 | 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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| 62 | tempData = [0.0 for i in range(numTimes)]
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| 63 | for idx, line in enumerate(lines):
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| 64 | # print(line)
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| 65 |
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| 66 | if currLocks == -1:
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| 67 | lineArr = line.split()
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| 68 | currLocks = lineArr[-1]
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| 69 | continue
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| 70 |
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| 71 | if line[0:5] == "cores":
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| 72 | continue
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| 73 |
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| 74 | if not nameSet:
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| 75 | nameSet = True
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| 76 | continue
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| 77 |
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| 78 | lineArr = line.split()
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| 79 | tempData[count] = float(lineArr[-1])
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| 80 | count += 1
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| 81 | if count == numTimes:
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| 82 | currMedian = median( tempData )
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| 83 | data[currVariant][procCount] = currMedian
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| 84 | lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData))
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| 85 | bars[currVariant][0][procCount] = currMedian - lower
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| 86 | bars[currVariant][1][procCount] = upper - currMedian
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| 87 | count = 0
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| 88 | procCount += 1
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| 89 |
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| 90 | if procCount == len(procs):
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| 91 | procCount = 0
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| 92 | nameSet = False
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| 93 | currVariant += 1
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| 94 |
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| 95 | if currVariant == numVariants:
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| 96 | fig, ax = plt.subplots()
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| 97 | plt.title(name + " Benchmark: " + str(currLocks) + " Locks")
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| 98 | plt.ylabel("Throughput (entries)")
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| 99 | plt.xlabel("Cores")
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| 100 | for idx, arr in enumerate(data):
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| 101 | plt.errorbar( procs, arr, [bars[idx][0], bars[idx][1]], capsize=2, marker='o' )
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| 102 | plt.yscale("log")
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| 103 | plt.xticks(procs)
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| 104 | ax.legend(names)
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| 105 | # fig.savefig("plots/" + machineName + "Aggregate_Lock_" + str(currLocks) + ".png")
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| 106 | plt.savefig("plots/" + machineName + "Aggregate_Lock_" + str(currLocks) + ".pgf")
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| 107 | fig.clf()
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| 108 |
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| 109 | # reset
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| 110 | currLocks = -1
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| 111 | currVariant = 0
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