52 lines
1.6 KiB
Python
Executable file
52 lines
1.6 KiB
Python
Executable file
#!/usr/bin/python3
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# Run it with
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# export CPU=0; /usr/bin/taskset --cpu-list $CPU ./bench.py $CPU
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import os
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import sys
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import timeit
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import numpy as np
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NSERIES = (128, )
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POWS = 2**np.arange(2, 23, dtype=int)
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# Size of one dimensional numpy arrays of dtype 'float64':
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# A fix overhead of 96 bytes plus a variable size:
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# (n_items x 8 bytes)
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def load_data_row(x, time_series):
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"""Store one time series per raw"""
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for row, ts in enumerate(time_series):
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x[row,:] = ts
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return x
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def load_data_column(x, time_series):
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"""Store one time series per raw"""
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for column, ts in enumerate(time_series):
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x[:, column] = ts
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return x
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if __name__ == '__main__':
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for nseries in NSERIES:
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print(30*'=', '\n', nseries)
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float_items = POWS
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byte_sizes = (float_items*8) #+ 96
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bads = []
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goods = []
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results = open(f'results_ns{nseries}', 'wt')
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for i, len_one_series in enumerate(float_items):
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time_series = np.zeros((nseries, len_one_series), dtype='float64')
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x = np.empty((nseries, len_one_series), dtype='float64')
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print('Timing good...')
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good = min(timeit.repeat(lambda: load_data_row(x, time_series), number=5))/5
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x = np.empty((len_one_series, nseries), dtype='float64')
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print('Timing bad...')
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bad = min(timeit.repeat(lambda: load_data_column(x, time_series), number=5))/5
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print(f'{len_one_series}/{POWS[-1]} {good} {bad}')
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bads.append(bad)
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goods.append(good)
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results.write(f'{byte_sizes[i]} {good} {bad}\n')
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results.flush()
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results.close()
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