| |
| import pandas as pd |
| import numpy as np |
| import argparse |
| from io import StringIO |
| parser = argparse.ArgumentParser(description='Prof parser') |
| parser.add_argument('file', type=str) |
|
|
| |
| def strip(text): |
| try: |
| return text.strip() |
| except AttributeError: |
| return text |
|
|
| def convert(x): |
| if x[-2:] == 'ms': |
| x = float(x[:-2]) |
| elif x[-2:] == 'us': |
| x = 0.001 * float(x[:-2]) |
| elif x[-2:] == 'ns': |
| x = 1e-6 * float(x[:-2]) |
| elif x[-1] == 's': |
| x = 1000 * float(x[:-1]) |
| return x |
|
|
| def convert_timep(x): |
| return float(x[:-1]) |
|
|
|
|
| def pandify(data): |
| names = ['Type', 'Time (%)', 'Time (ms)', 'Calls', 'Avg', 'Min', 'Max', |
| 'Name'] |
| convs = {i: strip for i in range(8)} |
| df = pd.read_csv(StringIO(data), names=names, skiprows=0, sep=';', |
| converters=convs) |
| df.loc[df.loc[:,'Type'] == '', 'Type'] = np.NaN |
| df = df.fillna(method='ffill') |
| df['Time (ms)'] = df['Time (ms)'].apply(convert) |
| df['Time (%)'] = df['Time (%)'].apply(convert_timep) |
| df['Calls'] = df['Calls'].apply(int) |
| df1 = df[df['Type'] == 'GPU activities:'] |
| df2 = df[df['Type'] == 'API calls:'] |
| idx = df1.Name.str.contains('CUDA memcpy') |
| s1 = pd.Series({'Type': 'Total:', |
| 'Time (%)': df1['Time (%)'].sum(), |
| 'Time (ms)': df1['Time (ms)'].sum(), |
| 'Calls': df1['Calls'].sum(), |
| 'Avg': '', 'Min': '', 'Max': '', 'Name': ''}) |
| s2 = pd.Series({'Type': 'Total (no mem):', |
| 'Time (%)': df1.loc[~idx, 'Time (%)'].sum(), |
| 'Time (ms)': df1.loc[~idx, 'Time (ms)'].sum(), |
| 'Calls': df1.loc[~idx, 'Calls'].sum(), |
| 'Avg': '', 'Min': '', 'Max': '', 'Name': ''}) |
| s3 = pd.Series({'Type': 'Total:', |
| 'Time (%)': df2['Time (%)'].sum(), |
| 'Time (ms)': df2['Time (ms)'].sum(), |
| 'Calls': df2['Calls'].sum(), |
| 'Avg': '', 'Min': '', 'Max': '', 'Name': ''}) |
|
|
| df3 = pd.concat([df1, pd.concat([s1, s2], axis=1).T, |
| df2, pd.concat([s3,], axis=1).T], |
| ignore_index=True, axis=0) |
| return df3 |
|
|
|
|
| def prep_file(file): |
| with open(file, 'r') as f: |
| data = f.readlines() |
|
|
| header = data[0].split('command: ')[1] |
| for i, l in enumerate(data): |
| if i >= 3: |
| data[i] = ';'.join([l[:16], l[17:25], l[26:35], l[36:45], |
| l[46:55], l[56:65], l[66:75], l[76:]]) |
| data = ''.join(data[4:]) |
| return data, header |
|
|
| if __name__ == '__main__': |
| args = parser.parse_args() |
| data, header = prep_file(args.file) |
| df = pandify(data) |
| with open(args.file, 'w') as f: |
| f.write(header) |
| f.write(df.to_string(index=False)) |
|
|
|
|
