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78 lines
3.4 KiB
Python
78 lines
3.4 KiB
Python
# ##########################################################################
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# Copyright (C) 2022 Advanced Micro Devices, Inc. All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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#
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# 1. Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
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# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
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# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
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# SUCH DAMAGE.
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# ##########################################################################
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import argparse
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import pandas as pd
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def append_units(label):
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if label.endswith('_time'):
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return label + '_us'
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else:
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return label
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def operation_count(n, function):
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"""Returns the number of operations required for a given function with an n-by-n matrix."""
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if function.startswith('getrf'):
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return (2 * n ** 3 / 3) - (n ** 2 / 2) - (n / 6)
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elif function.startswith('geqrf'):
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return (4 * n ** 3 / 3) + (2 * n ** 2) + (4 * n)
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elif function.startswith('getri'):
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return (4 * n ** 3 / 3) - (n ** 2) + (5 * n / 3)
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else:
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raise ValueError(f'Could not calculate operation count for {function}.')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description='Processes the rocsolver-benchmark-suite results.')
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parser.add_argument('input_path',
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help='the input file name for the raw benchmark results')
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parser.add_argument('output_path',
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help='the output file name for the processed benchmark results')
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args = parser.parse_args()
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# load the data and filter on the function name
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df = pd.read_csv(args.input_path, encoding='utf-8')
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# rename columns for nicer labels
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df.rename(append_units, axis='columns', inplace=True)
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# prepend name column
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df.insert(0, 'name', df['precision'].str.cat(df['function']))
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# the op_count derived column calculation can't handle a mix of functions
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if not df['function'].empty and (df['function'] != df['function'][0]).all():
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raise ValueError('This script can only handle one function at a time.')
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function = df['function'][0]
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# append derived columns
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df['mean_gpu_time_us_per_matrix'] = df['gpu_time_us'].astype(float) / df.get('batch_c', 1)
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df['op_count'] = operation_count(df['n'], function).round().astype(int) * df.get('batch_c', 1)
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df['performance_gflops'] = df['op_count'] / df['gpu_time_us'] / 1000
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# save results
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df.to_csv(args.output_path, encoding='utf-8', index=False)
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