Simple Kernel takes too long to accomplish, using GpuMat, possible mistake in memory accessing

I’ve got a very basic performance issue. The kernel in the code below needs 18ms to accomplish, even in this very simplified version. The occupancy of the kernel is theoretical 100%, practical 86%.

Functions implemented in OpenCV runnig on adequate speed, e.g. the cuda::gammaCorrection and the cuda::demosaicing both take just about 2ms per call.

After reading some other topics I think the problem is something with the memory accesses or the pointers to the GpuMats, but I am not sure how to fix it.

I am using Visual Studio 13 with Nsight and OpenCV 3.0.0 with Cuda 7.5 as it is availiable from My Machine: i7-5930K, 32GB DDR4, Titan X, Win 7x64.

Thank you for your help!

#include <opencv2\opencv.hpp>
#include <opencv2\core\cuda.hpp>
#include <opencv2\core\opengl.hpp>
#include <opencv2\cudaimgproc.hpp>
#include <opencv2\cudawarping.hpp>
#include <opencv2\cudalegacy.hpp>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"

cuda::GpuMat	d_mapx;
cuda::GpuMat	d_mapy;
const Size	size_sensor(5120, 3840);

void main(void)
     int	userQuit = 0;
     d_mapx = cuda::GpuMat(size_sensor, CV_32FC1);
     d_mapy = cuda::GpuMat(size_sensor, CV_32FC1);
     while (!userQuit)
          //other code
__global__ void createMapKernel( cuda::PtrStepSzf d_mapx, cuda::PtrStepSzf d_mapy, const int cols, const int rows, const float half_cols, const float half_rows, const float da, const float db)
	int dx = blockDim.x * blockIdx.x + threadIdx.x;
	int dy = blockDim.y * blockIdx.y + threadIdx.y;

	//Next two lines are the place holders for the real algorithm, 
        //which writes the results back in the same way
	d_mapx.ptr(dy)[dx] = dx;
	d_mapy.ptr(dy)[dx] = dy;
void createMapCuda(void)
	static float a;
	static float b;
	static float cos_i;

	static int cols = size_sensor.width;
	static int rows = size_sensor.height;

	static float half_rows = size_sensor.height / 2;
	static float half_cols = size_sensor.width / 2;

	a = (0.01 *f_distortionA);
	b = (0.01 *f_distortionB);

	static const dim3 dimBlock(16, 16);
	static const dim3 dimGrid((int)ceil(cols / dimBlock.x), (int)ceil(rows / dimBlock.y));

	createMapKernel << <dimGrid, dimBlock >> >(d_mapx, d_mapy, cols, rows, half_cols, half_rows, a, b);