kernels.cu File Reference

#include <cuda.h>
#include <cuda_runtime.h>

Functions
__global__ void	propogateInputKernel (float *inputs, float *weights, float *outputs, int numInputs, int sequenceId, int numOutputs)
	Calculates the euclidean distance between input vector and weight vector of a neuron and saves these distances in the output array.
__global__ void	findBestMatchPass1Kernel (float *outputs, int *winner, int numOutputs)
	Finds the best matching unit having the lowest euclidean distance - part 1/2.
__global__ void	findBestMatchPass2Kernel (float *outputs, int *winner, int numOutputs)
	Finds the best matching unit having the lowest euclidean distance - part 2/2.
__global__ void	updateWeightsKernel (float *inputs, float *weights, int *winner, float sigma, int numInputs, int sequenceId, int numOutputs, int neighbourhoodSize, float initLearningRate, int numIterations, int currentIteration)
	Updates weights of the self-organising map.
void	findBestMatchOnDevice (dim3 grid, dim3 block, float *outputs, int *winner, int numOutputs)
	Wrapper for findBestMatchPass1Kernel and findBestMatchPass2Kernel.
void	propogateInputOnDevice (dim3 grid, dim3 block, float *inputs, float *weights, float *outputs, int numInputs, int sequenceId, int numOutputs)
	Wrapper for propogateInputKernel.
void	updateWeightsOnDevice (dim3 grid, dim3 block, float *inputs, float *weights, int *winner, float sigma, int numInputs, int sequenceId, int numOutputs, int neighbourhoodSize, float initLearningRate, int numIterations, int currentIteration)
	Wrapper for updateWeightsKernel.

Function Documentation

void findBestMatchOnDevice	(	dim3	grid,
		dim3	block,
		float *	outputs,
		int *	winner,
		int	numOutputs
	)

Wrapper for findBestMatchPass1Kernel and findBestMatchPass2Kernel.

Parameters

[in]	grid	- CUDA grid size
[in]	block	- CUDA block size
[in]	outputs	- outputs
[in]	numOutputs	- number of outputs
[out]	winner	- winner

__global__ void findBestMatchPass1Kernel	(	float *	outputs,
		int *	winner,
		int	numOutputs
	)

Finds the best matching unit having the lowest euclidean distance - part 1/2.

Parameters

[in]	outputs	- outputs
[in]	numOutputs	- number of outputs
[out]	winner	- winner

__global__ void findBestMatchPass2Kernel	(	float *	outputs,
		int *	winner,
		int	numOutputs
	)

Finds the best matching unit having the lowest euclidean distance - part 2/2.

Parameters

[in]	outputs	- outputs
[in]	numOutputs	- number of outputs
[out]	winner	- winner

__global__ void propogateInputKernel	(	float *	inputs,
		float *	weights,
		float *	outputs,
		int	numInputs,
		int	sequenceId,
		int	numOutputs
	)

Calculates the euclidean distance between input vector and weight vector of a neuron and saves these distances in the output array.

Parameters

[in]	inputs	- inputs
[in]	weights	- weights
[in]	numInputs	- number of inputs
[in]	sequenceId	- sequence id
[in]	numOutputs	- number of outputs
[out]	outputs	- outputs

void propogateInputOnDevice	(	dim3	grid,
		dim3	block,
		float *	inputs,
		float *	weights,
		float *	outputs,
		int	numInputs,
		int	sequenceId,
		int	numOutputs
	)

Wrapper for propogateInputKernel.

Parameters

[in]	grid	- CUDA grid size
[in]	block	- CUDA block size
[in]	inputs	- inputs
[in]	weights	- weights
[in]	numInputs	- number of inputs
[in]	sequenceId	- sequence id
[in]	numOutputs	- number of outputs
[out]	outputs	- outputs

__global__ void updateWeightsKernel	(	float *	inputs,
		float *	weights,
		int *	winner,
		float	sigma,
		int	numInputs,
		int	sequenceId,
		int	numOutputs,
		int	neighbourhoodSize,
		float	initLearningRate,
		int	numIterations,
		int	currentIteration
	)

Updates weights of the self-organising map.

Parameters

[in]	inputs	- inputs
[in]	winner	- winner
[in]	sigma	- sigma
[in]	numInputs	- number of inputs
[in]	sequenceId	- sequence id
[in]	numOutputs	- number of outputs
[in]	neighbourhoodSize	- neighbourhood size
[in]	numIterations	- number of iterations
[in]	currentIteration	- current iteration
[out]	weights	- weights

void updateWeightsOnDevice	(	dim3	grid,
		dim3	block,
		float *	inputs,
		float *	weights,
		int *	winner,
		float	sigma,
		int	numInputs,
		int	sequenceId,
		int	numOutputs,
		int	neighbourhoodSize,
		float	initLearningRate,
		int	numIterations,
		int	currentIteration
	)

Wrapper for updateWeightsKernel.

Parameters

[in]	grid	- CUDA grid size
[in]	block	- CUDA block size
[in]	inputs	- inputs
[in]	winner	- winner
[in]	sigma	- sigma
[in]	numInputs	- number of inputs
[in]	sequenceId	- sequence id
[in]	numOutputs	- number of outputs
[in]	neighbourhoodSize	- neighbourhood size
[in]	numIterations	- number of iterations
[in]	currentIteration	- current iteration
[out]	weights	- weights