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# This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This file follows the
# PEP8 Python style guide and uses a max-width of 120 characters per line.
#
# Author(s):
# Cedric Nugteren <www.cedricnugteren.nl>
import sys
import database.clblast as clblast
def get_best_results(database):
"""Retrieves the results with the lowest execution times"""
sections_best = []
for section in database["sections"]:
section_best = {}
# Stores all the section's meta data
for attribute in section.keys():
if attribute != "results":
section_best[attribute] = section[attribute]
if section_best["clblast_device_architecture"] == "" and section_best["clblast_device_vendor"] in clblast.VENDORS_WITH_ARCHITECTURE:
section_best["clblast_device_architecture"] = clblast.DEVICE_ARCHITECTURE_DEFAULT
# Find the best result
parameters_best = None
time_best = sys.float_info.max
for result in section["results"]:
if result["time"] < time_best:
time_best = result["time"]
parameters_best = result["parameters"]
# Stores the best result
section_best["results"] = [{"time": time_best, "parameters": parameters_best}]
sections_best.append(section_best)
return {"sections": sections_best}
def get_relative_bests(name, common_results, common_parameters, verbose=False):
"""Retrieves the parameters with the relative best execution time over different devices"""
# Helper function
def argmin(iterable):
return min(enumerate(iterable), key=lambda x: x[1])[0]
# Computes the sum of the execution times over the different devices
performance_sums = []
for parameters in common_parameters:
performance_sum = sum([r["relative_time"] for r in common_results if r["parameters"] == parameters])
performance_sums.append(performance_sum)
# Retrieves the entry with the lowest time
best_index = argmin(performance_sums)
best_performance = performance_sums[best_index]
best_parameters = common_parameters[best_index]
# Completed, report and return the results
if verbose:
print("[database] " + str(name) + " with performance " + str(best_performance))
return best_parameters
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