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92 lines
3.2 KiB
Python
92 lines
3.2 KiB
Python
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# Based on work: https://int-i.github.io/python/2021-11-07/matplotlib-google-benchmark-visualization/
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# Modified by: Bensuperpc
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from argparse import ArgumentParser
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from itertools import groupby
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import json
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import operator
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import matplotlib.pyplot as plt
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import numpy as np
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import seaborn as sns
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import pandas as pd
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# Extract the benchmark name from the benchmark name string
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def extract_label_from_benchmark(benchmark):
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bench_full_name = benchmark['name']
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bench_name = bench_full_name.split('/')[0] # Remove all after /
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if (bench_name.startswith('BM_')): # Remove if string start with BM_
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return bench_name[3:] # Remove BM_
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else:
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return bench_name
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# Extract the benchmark size from the benchmark
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def extract_size_from_benchmark(benchmark):
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bench_name = benchmark['name']
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return bench_name.split('/')[1] # Remove all before /
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if __name__ == "__main__":
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# ./prog_name --benchmark_format=json --benchmark_out=result.json
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parser = ArgumentParser()
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parser.add_argument('path', help='benchmark result json file')
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args = parser.parse_args()
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with open(args.path) as file:
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benchmark_result = json.load(file)
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benchmarks = benchmark_result['benchmarks']
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elapsed_times = groupby(benchmarks, extract_label_from_benchmark)
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data1 = None
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data2 = None
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for key, group in elapsed_times:
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benchmark = list(group)
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x = list(map(extract_size_from_benchmark, benchmark))
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y1 = list(map(operator.itemgetter('bytes_per_second'), benchmark))
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y2 = list(map(operator.itemgetter('items_per_second'), benchmark))
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if data1 is None:
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data1 = pd.DataFrame({'size': x, key: y1})
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else:
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data1[key] = y1
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if data2 is None:
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data2 = pd.DataFrame({'size': x, key: y2})
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else:
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data2[key] = y2
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df1 = pd.melt(data1, id_vars=['size'], var_name='Benchmark', value_name='bytes_per_second')
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df1_max_indices = df1.groupby(['size', 'Benchmark'])['bytes_per_second'].transform(max) == df1['bytes_per_second']
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df1 = df1.loc[df1_max_indices]
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df1.reset_index(drop=True, inplace=True)
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df2 = pd.melt(data2, id_vars=['size'], var_name='Benchmark', value_name='items_per_second')
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df2_max_indices = df2.groupby(['size', 'Benchmark'])['items_per_second'].transform(max) == df2['items_per_second']
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df2 = df2.loc[df2_max_indices]
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df2.reset_index(drop=True, inplace=True)
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sns.set_theme()
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fig, ax = plt.subplots(2, 1)
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fig.set_size_inches(16, 9)
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fig.set_dpi(96)
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sns.lineplot(data=df1, x='size', y='bytes_per_second', hue='Benchmark', ax=ax[0])
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sns.lineplot(data=df2, x='size', y='items_per_second', hue='Benchmark', ax=ax[1])
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ax[0].set_title('Bytes per second')
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ax[1].set_title('Items per second')
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ax[0].set_xlabel('Array size')
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ax[1].set_xlabel('Array size')
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ax[0].set_ylabel('byte per second')
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ax[1].set_ylabel('items per second')
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fig.tight_layout()
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plt.savefig('benchmark.png', bbox_inches='tight', dpi=300)
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plt.show()
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