Assume that the stochastic distribution of the execution time while fixing discrete variables corresponds to a Gaussian Process. More...

#include <BayesianClusterSearch.h>

Inheritance diagram for autopas::BayesianClusterSearch:

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Collaboration diagram for autopas::BayesianClusterSearch:

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Public Member Functions
	BayesianClusterSearch (const InteractionTypeOption &interactionType, const std::set< ContainerOption > &allowedContainerOptions=ContainerOption::getAllOptions(), const NumberSet< double > &allowedCellSizeFactors=NumberInterval< double >(1., 2.), const std::set< TraversalOption > &allowedTraversalOptions=TraversalOption::getAllOptions(), const std::set< LoadEstimatorOption > &allowedLoadEstimatorOptions=LoadEstimatorOption::getAllOptions(), const std::set< DataLayoutOption > &allowedDataLayoutOptions=DataLayoutOption::getAllOptions(), const std::set< Newton3Option > &allowedNewton3Options=Newton3Option::getAllOptions(), size_t maxEvidence=10, AcquisitionFunctionOption predAcqFunction=AcquisitionFunctionOption::upperConfidenceBound, const std::string &outputSuffix="", size_t predNumLHSamples=50, unsigned long seed=std::random_device()())
	Constructor.

TuningStrategyOption	getOptionType () const override
	Get this object's associated TuningStrategyOption type.

void	addEvidence (const Configuration &configuration, const Evidence &evidence) override
	Notifies the strategy about empirically collected information for the given configuration.

bool	reset (size_t iteration, size_t tuningPhase, std::vector< Configuration > &configQueue, const autopas::EvidenceCollection &evidenceCollection) override
	Reset all internal parameters to the beginning of a new tuning phase.

bool	optimizeSuggestions (std::vector< Configuration > &configQueue, const EvidenceCollection &evidenceCollection) override
	Optimizes the queue of configurations to process.

void	rejectConfiguration (const Configuration &configuration, bool indefinitely) override
	Notify the strategy about a configuration that is (currently) invalid and thus can potentially be dropped from some internal storage.

bool	searchSpaceIsEmpty () const
	Indicate if the search space is empty.

bool	needsSmoothedHomogeneityAndMaxDensity () const override
	Indicate whether the strategy needs smoothed values of homogeneity and max density.

Public Member Functions inherited from autopas::TuningStrategyInterface
virtual TuningStrategyOption	getOptionType () const =0
	Get this object's associated TuningStrategyOption type.

virtual void	addEvidence (const Configuration &configuration, const Evidence &evidence)
	Notifies the strategy about empirically collected information for the given configuration.

virtual bool	optimizeSuggestions (std::vector< Configuration > &configQueue, const EvidenceCollection &evidenceCollection)=0
	Optimizes the queue of configurations to process.

virtual bool	reset (size_t iteration, size_t tuningPhase, std::vector< Configuration > &configQueue, const autopas::EvidenceCollection &evidenceCollection)=0
	Reset all internal parameters to the beginning of a new tuning phase.

virtual bool	needsLiveInfo () const
	Returns whether this tuning strategy wants to get a LiveInfo object passed before a new tuning phase.

virtual void	receiveLiveInfo (const LiveInfo &info)
	Virtual method that subclasses can override to receive the LiveInfo object before a tuning phase if they return true in needsLiveInfo().

virtual void	rejectConfiguration (const Configuration &configuration, bool indefinitely)
	Notify the strategy about a configuration that is (currently) invalid and thus can potentially be dropped from some internal storage.

virtual bool	needsSmoothedHomogeneityAndMaxDensity () const
	Indicate whether the strategy needs smoothed values of homogeneity and max density.

virtual void	receiveSmoothedHomogeneityAndMaxDensity (double homogeneity, double maxDensity)
	Method to pass smoothed homogeneity and the maximal density to the tuning strategy.

Detailed Description

Assume that the stochastic distribution of the execution time while fixing discrete variables corresponds to a Gaussian Process.

This allows to estimate the 'gain' of testing a given feature next.

Constructor & Destructor Documentation

◆ BayesianClusterSearch()

autopas::BayesianClusterSearch::BayesianClusterSearch	(	const InteractionTypeOption &	interactionType,
		const std::set< ContainerOption > &	allowedContainerOptions = `ContainerOption::getAllOptions()`,
		const NumberSet< double > &	allowedCellSizeFactors = `NumberInterval<double>(1., 2.)`,
		const std::set< TraversalOption > &	allowedTraversalOptions = `TraversalOption::getAllOptions()`,
		const std::set< LoadEstimatorOption > &	allowedLoadEstimatorOptions = `LoadEstimatorOption::getAllOptions()`,
		const std::set< DataLayoutOption > &	allowedDataLayoutOptions = `DataLayoutOption::getAllOptions()`,
		const std::set< Newton3Option > &	allowedNewton3Options = `Newton3Option::getAllOptions()`,
		size_t	maxEvidence = `10`,
		AcquisitionFunctionOption	predAcqFunction = `AcquisitionFunctionOption::upperConfidenceBound`,
		const std::string &	outputSuffix = `""`,
		size_t	predNumLHSamples = `50`,
		unsigned long	seed = `std::random_device()()`
	)

explicit

Constructor.

Parameters

interactionType
allowedContainerOptions
allowedCellSizeFactors
allowedTraversalOptions
allowedLoadEstimatorOptions
allowedDataLayoutOptions
allowedNewton3Options
maxEvidence	Stop tuning after given number of evidence provided.
predAcqFunction	Acquisition function used for prediction while tuning.
outputSuffix	Suffix for output logger.
predNumLHSamples	Number of latin-hypercube-samples used to find a evidence with high predicted acquisition
seed	Seed of random number generator (should only be used for tests)

Member Function Documentation

◆ addEvidence()

void autopas::BayesianClusterSearch::addEvidence	(	const Configuration &	configuration,
		const Evidence &	evidence
	)

overridevirtual

Notifies the strategy about empirically collected information for the given configuration.

All evidence is stored centrally in the AutoTuner and its EvidenceCollection is passed to the tuning strategies during optimization.

Implementing this function is only necessary if the tuning strategy processes evidence differently than EvidenceCollection.

Parameters

configuration	Configuration used to obtain the evidence.
evidence	Measurement and when it was taken.

Reimplemented from autopas::TuningStrategyInterface.

◆ getOptionType()

autopas::TuningStrategyOption autopas::BayesianClusterSearch::getOptionType ( ) const

overridevirtual

Get this object's associated TuningStrategyOption type.

Returns: TuningStrategyOption

Implements autopas::TuningStrategyInterface.

◆ needsSmoothedHomogeneityAndMaxDensity()

bool autopas::BayesianClusterSearch::needsSmoothedHomogeneityAndMaxDensity ( ) const

inlineoverridevirtual

Indicate whether the strategy needs smoothed values of homogeneity and max density.

Returns

Reimplemented from autopas::TuningStrategyInterface.

◆ optimizeSuggestions()

bool autopas::BayesianClusterSearch::optimizeSuggestions	(	std::vector< Configuration > &	configQueue,
		const EvidenceCollection &	evidenceCollection
	)

overridevirtual

Optimizes the queue of configurations to process.

This function is called once before each iteration in a tuning phase so all tuning strategies can give their input on which configuration to try next. This is done by reordering configQueue so that the next configuration to try is at the end (FIFO).

Parameters

configQueue	Queue of configurations to be tested. The tuning strategy should edit this queue.
evidenceCollection	All collected evidence until now.

Returns: boolean value to signal if the tuning strategy has intentionally wiped the config queue

Implements autopas::TuningStrategyInterface.

◆ rejectConfiguration()

void autopas::BayesianClusterSearch::rejectConfiguration	(	const Configuration &	configuration,
		bool	indefinitely
	)

overridevirtual

Notify the strategy about a configuration that is (currently) invalid and thus can potentially be dropped from some internal storage.

Parameters

configuration
indefinitely	Whether the given configuration will never be valid

Reimplemented from autopas::TuningStrategyInterface.

◆ reset()

bool autopas::BayesianClusterSearch::reset	(	size_t	iteration,
		size_t	tuningPhase,
		std::vector< Configuration > &	configQueue,
		const autopas::EvidenceCollection &	evidenceCollection
	)

overridevirtual

Reset all internal parameters to the beginning of a new tuning phase.

This can also mean to reorder the configQueue to some initially expected state.

Parameters

iteration	Gives the current iteration to the tuning strategy.
tuningPhase	Gives the current tuning phase to the tuning strategy.
configQueue	Queue of configurations to be tested. The tuning strategy should edit this queue.
evidenceCollection	All collected evidence until now.

Returns: boolean value to signal if the tuning strategy has intentionally wiped the config queue

Implements autopas::TuningStrategyInterface.

◆ searchSpaceIsEmpty()

bool autopas::BayesianClusterSearch::searchSpaceIsEmpty ( ) const

Indicate if the search space is empty.

Returns

The documentation for this class was generated from the following files:

autopas/tuning/tuningStrategy/BayesianClusterSearch.h
autopas/tuning/tuningStrategy/BayesianClusterSearch.cpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ BayesianClusterSearch()

Member Function Documentation

◆ addEvidence()

◆ getOptionType()

◆ needsSmoothedHomogeneityAndMaxDensity()

◆ optimizeSuggestions()

◆ rejectConfiguration()

◆ reset()

◆ searchSpaceIsEmpty()