| [14e1053] | 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 | 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 processor for side_chan bench
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| 42 | line = readfile.readline()
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| 43 | sideChanProcs = []
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| 44 | for val in line.split():
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| 45 | sideChanProcs.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 | def sci_format(x, pos):
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| 56 | return '{:.1e}'.format(x).replace('+0', '')
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| 57 |
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| 58 | def sci_format_label(x):
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| 59 | return '{:.2e}'.format(x).replace('+0', '')
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| 60 |
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| 61 | class Bench(Enum):
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| 62 | Unset = 0
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| 63 | Contend2 = 1
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| 64 | Contend4 = 2
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| 65 | Contend8 = 3
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| 66 | Spin2 = 4
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| 67 | Spin4 = 5
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| 68 | Spin8 = 6
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| 69 | SideChan = 7
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| 70 | Future = 8
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| 71 | Order = 9
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| 72 |
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| 73 | nameSet = False
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| 74 | currBench = Bench.Unset # default val
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| 75 | count = 0
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| 76 | procCount = 0
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| 77 | currVariant = 0
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| 78 | name = ""
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| 79 | title = ""
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| 80 | experiment_duration = 10.0
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| 81 | var_name = ""
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| 82 | future_variants=["CFA", "uC++"]
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| 83 | future_names=["OR", "AND", "AND-OR", "OR-AND"]
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| 84 | future_data=[[0.0 for i in range(len(future_names))] for j in range(2)]
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| 85 | future_bars=[[[0.0 for i in range(len(future_names))],[0.0 for k in range(len(future_names))]] for j in range(2)]
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| 86 | curr_future=0
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| 87 | sendData = [0.0 for j in range(numVariants)]
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| 88 | data = [[0.0 for i in range(len(procs))] for j in range(numVariants)]
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| 89 | 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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| 90 | sideData = [[0.0 for i in range(len(sideChanProcs))] for j in range(numVariants)]
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| 91 | sideBars = [[[0.0 for i in range(len(sideChanProcs))],[0.0 for k in range(len(sideChanProcs))]] for j in range(numVariants)]
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| 92 | tempData = [0.0 for i in range(numTimes)]
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| 93 | orderData = [0.0 for i in range(numVariants)]
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| 94 | for idx, line in enumerate(lines):
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| 95 | # print(line)
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| 96 |
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| 97 | if currBench == Bench.Unset:
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| 98 | if line == "contend2:":
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| 99 | name = "Contend_2"
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| 100 | title = "2 Clause Contend"
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| 101 | currBench = Bench.Contend2
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| 102 | elif line == "contend4:":
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| 103 | name = "Contend_4"
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| 104 | title = "4 Clause Contend"
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| 105 | currBench = Bench.Contend4
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| 106 | elif line == "contend8:":
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| 107 | name = "Contend_8"
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| 108 | title = "8 Clause Contend"
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| 109 | currBench = Bench.Contend8
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| 110 | elif line == "spin2:":
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| 111 | name = "Spin_2"
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| 112 | title = "2 Clause Spin"
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| 113 | currBench = Bench.Spin2
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| 114 | elif line == "spin4:":
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| 115 | name = "Spin_4"
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| 116 | title = "4 Clause Spin"
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| 117 | currBench = Bench.Spin4
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| 118 | elif line == "spin8:":
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| 119 | name = "Spin_8"
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| 120 | title = "8 Clause Spin"
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| 121 | currBench = Bench.Spin8
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| 122 | elif line == "sidechan:":
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| 123 | name = "Sidechan"
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| 124 | currBench = Bench.SideChan
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| 125 | elif line[0:6] == "future":
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| 126 | name = "Future"
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| 127 | title = "Future Synchronization"
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| 128 | currBench = Bench.Future
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| 129 | elif line == "order:":
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| 130 | name = "order"
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| 131 | currBench = Bench.Order
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| 132 | else:
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| 133 | print("Expected benchmark name")
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| 134 | print("Line: " + line)
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| 135 | sys.exit()
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| 136 | continue
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| 137 |
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| 138 | if line[0:5] == "cores":
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| 139 | continue
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| 140 |
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| 141 | if not nameSet:
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| 142 | nameSet = True
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| 143 | continue
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| 144 |
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| 145 | lineArr = line.split()
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| 146 | tempData[count] = float(lineArr[-1]) / experiment_duration
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| 147 | count += 1
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| 148 |
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| 149 | if currBench == Bench.Future:
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| 150 | if count == numTimes:
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| 151 | currMedian = median( tempData )
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| 152 | future_data[currVariant][curr_future] = currMedian
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| 153 | lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData))
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| 154 | future_bars[currVariant][0][curr_future] = currMedian - lower
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| 155 | future_bars[currVariant][1][curr_future] = upper - currMedian
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| 156 | count = 0
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| 157 | nameSet = False
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| 158 | currVariant += 1
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| 159 | if currVariant == 2:
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| 160 | curr_future += 1
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| 161 | # reset
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| 162 | currBench = Bench.Unset
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| 163 | currVariant = 0
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| 164 | if curr_future == len(future_names):
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| 165 | x = np.arange(len(future_names)) # the label locations
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| 166 | width = 0.45 # the width of the bars
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| 167 | multiplier = .5
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| 168 | fig, ax = plt.subplots(layout='constrained')
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| 169 | plt.title(title + " Benchmark")
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| 170 | plt.ylabel("Throughput (statement completions per second)")
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| 171 | plt.xlabel("Operation")
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| 172 | ax.yaxis.set_major_formatter(ticks.FuncFormatter(sci_format))
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| 173 | for idx, arr in enumerate(future_data):
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| 174 | offset = width * multiplier
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| 175 | rects = ax.bar(x + offset, arr, width, label=future_variants[idx], yerr=[future_bars[idx][0], future_bars[idx][1]])
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| 176 | # ax.bar_label(rects, padding=3, fmt='%.1e')
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| 177 | ax.bar_label(rects, padding=3, fmt=sci_format_label)
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| 178 | multiplier += 1
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| 179 | plt.xticks(x + width, future_names)
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| 180 |
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| 181 | ax.legend(future_variants, loc='lower right')
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| 182 | # fig.savefig("plots/" + machineName + name + ".png")
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| 183 | plt.savefig("plots/" + machineName + name + ".pgf")
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| 184 | fig.clf()
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| 185 |
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| 186 | elif currBench == Bench.Order:
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| 187 | if count == numTimes:
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| 188 | currMedian = median( tempData )
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| 189 | orderData[currVariant] = currMedian
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| 190 | count = 0
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| 191 | currVariant += 1
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| 192 | procCount = 0
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| 193 | nameSet = False
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| 194 | if currVariant == numVariants:
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| 195 | fileName = "data/" + machineName + "Order"
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| 196 | f = open(fileName, 'w')
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| 197 | f.write(" & ".join(map(lambda a: str(int(a)), orderData)))
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| 198 |
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| 199 | # reset
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| 200 | currBench = Bench.Unset
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| 201 | currVariant = 0
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| 202 |
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| 203 | elif currBench == Bench.SideChan:
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| 204 | if count == numTimes:
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| 205 | currMedian = median( tempData )
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| 206 | sideData[currVariant][procCount] = currMedian
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| 207 | lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData))
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| 208 | sideBars[currVariant][0][procCount] = currMedian - lower
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| 209 | sideBars[currVariant][1][procCount] = upper - currMedian
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| 210 | count = 0
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| 211 | procCount += 1
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| 212 | if procCount == len(sideChanProcs):
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| 213 | procCount = 0
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| 214 | nameSet = False
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| 215 | currVariant += 1
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| 216 |
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| 217 | if currVariant == numVariants:
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| 218 | fig, ax = plt.subplots()
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| 219 | plt.title(name + " Benchmark")
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| 220 | plt.ylabel("Throughput (channel operations per second)")
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| 221 | plt.xlabel("Cores")
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| 222 | ax.yaxis.set_major_formatter(ticks.FuncFormatter(sci_format))
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| 223 | for idx, arr in enumerate(sideData):
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| 224 | plt.errorbar( sideChanProcs, arr, [sideBars[idx][0], sideBars[idx][1]], capsize=2, marker=next(marker) )
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| 225 | plt.xticks(sideChanProcs)
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| 226 | marker = itertools.cycle(('o', 's', 'D', 'x', 'p', '^', 'h', '*', 'v' ))
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| 227 | # plt.yscale("log")
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| 228 | ax.legend(names)
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| 229 | # fig.savefig("plots/" + machineName + name + ".png")
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| 230 | plt.savefig("plots/" + machineName + name + ".pgf")
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| 231 | fig.clf()
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| 232 |
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| 233 | # reset
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| 234 | currBench = Bench.Unset
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| 235 | currVariant = 0
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| 236 | else:
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| 237 | if count == numTimes:
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| 238 | currMedian = median( tempData )
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| 239 | data[currVariant][procCount] = currMedian
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| 240 | lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData))
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| 241 | bars[currVariant][0][procCount] = currMedian - lower
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| 242 | bars[currVariant][1][procCount] = upper - currMedian
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| 243 | count = 0
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| 244 | procCount += 1
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| 245 |
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| 246 | if procCount == len(procs):
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| 247 | procCount = 0
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| 248 | nameSet = False
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| 249 | currVariant += 1
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| 250 |
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| 251 | if currVariant == numVariants:
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| 252 | fig, ax = plt.subplots(layout='constrained')
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| 253 | plt.title(title + " Benchmark")
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| 254 | plt.ylabel("Throughput (channel operations per second)")
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| 255 | plt.xlabel("Cores")
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| 256 | ax.yaxis.set_major_formatter(ticks.FuncFormatter(sci_format))
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| 257 | for idx, arr in enumerate(data):
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| 258 | plt.errorbar( procs, arr, [bars[idx][0], bars[idx][1]], capsize=2, marker=next(marker) )
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| 259 | plt.xticks(procs)
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| 260 | marker = itertools.cycle(('o', 's', 'D', 'x', 'p', '^', 'h', '*', 'v' ))
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| 261 | # plt.yscale("log")
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| 262 | # plt.ylim(1, None)
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| 263 | # ax.get_yaxis().set_major_formatter(ticks.ScalarFormatter())
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| 264 | # else:
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| 265 | # plt.ylim(0, None)
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| 266 | ax.legend(names)
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| 267 | # fig.savefig("plots/" + machineName + name + ".png")
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| 268 | plt.savefig("plots/" + machineName + name + ".pgf")
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| 269 | fig.clf()
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| 270 |
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| 271 | # reset
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| 272 | currBench = Bench.Unset
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| 273 | currVariant = 0
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