local

Documentation

Index

• type ComponentFeatures
• type PlaformFeatures
• type RatioPowerModel
  • func (r *RatioPowerModel) AddDesiredOutValue(y float64)
  • func (r *RatioPowerModel) AddNodeFeatureValues(x []float64)
  • func (r *RatioPowerModel) AddProcessFeatureValues(x []float64)
  • func (r *RatioPowerModel) GetComponentsPower(isIdlePower bool) ([]source.NodeComponentsEnergy, error)
  • func (r *RatioPowerModel) GetGPUPower(isIdlePower bool) ([]float64, error)
  • func (r *RatioPowerModel) GetModelType() types.ModelType
  • func (r *RatioPowerModel) GetNodeFeatureNamesList() []string
  • func (r *RatioPowerModel) GetPlatformPower(isIdlePower bool) ([]float64, error)
  • func (r *RatioPowerModel) GetProcessFeatureNamesList() []string
  • func (r *RatioPowerModel) IsEnabled() bool
  • func (r *RatioPowerModel) ResetSampleIdx()
  • func (r *RatioPowerModel) Train() error

Types

type ComponentFeatures

type ComponentFeatures int

The ratio features list follows this order

const (
	PkgUsageMetric ComponentFeatures = iota
	CoreUsageMetric
	DramUsageMetric
	UncoreUsageMetric
	OtherUsageMetric
	GpuUsageMetric
	PkgDynPower
	CoreDynPower
	DramDynPower
	UncoreDynPower
	OtherDynPower
	GpuDynPower
	PkgIdlePower
	CoreIdlePower
	DramIdlePower
	UncoreIdlePower
	OtherIdlePower
	GpuIdlePower
)

The ratio power model can be used for both node Component and Platform power estimation. The models for Component and Platform power estimation have different number of features.

type PlaformFeatures

type PlaformFeatures int

const (
	// Node features are at the end of the feature list
	PlatformUsageMetric PlaformFeatures = iota
	PlatformDynPower
	PlatformIdlePower
)

type RatioPowerModel

type RatioPowerModel struct {
	NodeFeatureNames    []string
	ProcessFeatureNames []string
	// contains filtered or unexported fields
}

RatioPowerModel stores the feature samples of each process for prediction The feature will added ordered from cycles, instructions and cache-misses

func (*RatioPowerModel) AddDesiredOutValue

func (r *RatioPowerModel) AddDesiredOutValue(y float64)

AddDesiredOutValue adds the the y, which is the response variable (or the dependent variable) of regression. RatioPowerModel is not trained, then we cannot Add training samples.

func (*RatioPowerModel) AddNodeFeatureValues

func (r *RatioPowerModel) AddNodeFeatureValues(x []float64)

AddNodeFeatureValues adds the the x for prediction, which is the variable used to calculate the ratio. RatioPowerModel is not trained, then we cannot Add training samples, only samples for prediction.

func (*RatioPowerModel) AddProcessFeatureValues

func (r *RatioPowerModel) AddProcessFeatureValues(x []float64)

AddProcessFeatureValues adds the the x for prediction, which is the variable used to calculate the ratio. RatioPowerModel is not trained, then we cannot Add training samples, only samples for prediction.

func (*RatioPowerModel) GetComponentsPower

func (r *RatioPowerModel) GetComponentsPower(isIdlePower bool) ([]source.NodeComponentsEnergy, error)

GetComponentsPower applies each component's ModelWeight prediction and return a map of component powers

func (*RatioPowerModel) GetGPUPower

func (r *RatioPowerModel) GetGPUPower(isIdlePower bool) ([]float64, error)

GetComponentsPower returns GPU Power in Watts associated to each each process/process/pod

func (*RatioPowerModel) GetModelType

func (r *RatioPowerModel) GetModelType() types.ModelType

GetModelType returns the model type

func (*RatioPowerModel) GetNodeFeatureNamesList

func (r *RatioPowerModel) GetNodeFeatureNamesList() []string

GetNodeFeatureNamesList returns the list of process features that the model was configured to use

func (*RatioPowerModel) GetPlatformPower

func (r *RatioPowerModel) GetPlatformPower(isIdlePower bool) ([]float64, error)

GetPlatformPower applies ModelWeight prediction and return a list of total powers

func (*RatioPowerModel) GetProcessFeatureNamesList

func (r *RatioPowerModel) GetProcessFeatureNamesList() []string

GetProcessFeatureNamesList returns the list of process features that the model was configured to use

func (*RatioPowerModel) IsEnabled

func (r *RatioPowerModel) IsEnabled() bool

IsEnabled returns true as Ratio Power model is always active

func (*RatioPowerModel) ResetSampleIdx

func (r *RatioPowerModel) ResetSampleIdx()

ResetSampleIdx set the sample vector index to 0 to overwrite the old samples with new ones for trainning or prediction.

func (*RatioPowerModel) Train

func (r *RatioPowerModel) Train() error

RatioPowerModel is not trained, then this function does nothing.