def tune(runner, kernel_options, device_options, tuning_options):
""" Find the best performing kernel configuration in the parameter space
:params runner: A runner from kernel_tuner.runners
:type runner: kernel_tuner.runner
:param kernel_options: A dictionary with all options for the kernel.
:type kernel_options: dict
:param device_options: A dictionary with all options for the device
on which the kernel should be tuned.
:type device_options: dict
:param tuning_options: A dictionary with all options regarding the tuning
process.
:type tuning_options: dict
:returns: A list of dictionaries for executed kernel configurations and their
execution times. And a dictionary that contains a information
about the hardware/software environment on which the tuning took place.
:rtype: list(dict()), dict()
"""
results = []
cache = {}
method = tuning_options.method
#scale variables in x to make 'eps' relevant for multiple variables
tuning_options["scaling"] = True
bounds, x0, eps = get_bounds_x0_eps(tuning_options)
kwargs = setup_method_arguments(method, bounds)
options = setup_method_options(method, tuning_options)
kwargs['options'] = options
args = (kernel_options, tuning_options, runner, results, cache)
minimizer_kwargs = dict(**kwargs)
minimizer_kwargs["method"] = method
minimizer_kwargs["args"] = args
opt_result = scipy.optimize.basinhopping(_cost_func,
x0,
stepsize=eps,
minimizer_kwargs=minimizer_kwargs,
disp=tuning_options.verbose)
if tuning_options.verbose:
print(opt_result.message)
return results, runner.dev.get_environment()
def tune(runner, kernel_options, device_options, tuning_options):
""" Find the best performing kernel configuration in the parameter space
:params runner: A runner from kernel_tuner.runners
:type runner: kernel_tuner.runner
:param kernel_options: A dictionary with all options for the kernel.
:type kernel_options: dict
:param device_options: A dictionary with all options for the device
on which the kernel should be tuned.
:type device_options: dict
:param tuning_options: A dictionary with all options regarding the tuning
process.
:type tuning_options: dict
:returns: A list of dictionaries for executed kernel configurations and their
execution times. And a dictionary that contains a information
about the hardware/software environment on which the tuning took place.
:rtype: list(dict()), dict()
"""
results = []
cache = {}
method = tuning_options.method
#scale variables in x to make 'eps' relevant for multiple variables
tuning_options["scaling"] = True
bounds, x0, eps = get_bounds_x0_eps(tuning_options)
kwargs = setup_method_arguments(method, bounds)
options = setup_method_options(method, tuning_options)
kwargs['options'] = options
args = (kernel_options, tuning_options, runner, results, cache)
minimizer_kwargs = dict(**kwargs)
minimizer_kwargs["method"] = method
minimizer_kwargs["args"] = args
opt_result = scipy.optimize.basinhopping(_cost_func, x0, stepsize=eps, minimizer_kwargs=minimizer_kwargs, disp=tuning_options.verbose)
if tuning_options.verbose:
print(opt_result.message)
return results, runner.dev.get_environment()
def test_setup_method_arguments():
# check if returns a dict, the specific options depend on scipy
assert isinstance(minimize.setup_method_arguments("bla", 5), dict)