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1. Dim3 grid calculation how to#
2. Dim3 grid calculation code#

That is we must use two dimensional blocks. You are asked to calculate the index of this image pixels in the kernel function, but to run kernel the blocks are 16x16 sizes. There are ways to do calculations in DP without DP units. You have a kernel called 'ProcessImge(dprtIn, dptrOut)'.

Dim3 grid calculation code#

You may want to test your code on another GPU or use single precision and check if it runs noticeably faster. (Inaccurate time measuring may be a problem, too.) defined using respectively dim3 threads(,) and dim3 grid(,). On my GPU without doing any calculations I still get about 20% of the runtime with calculations (without optimization flags). After discretization, the evolution equation becomes (Fig. GPUs reach maximal performance as the problem size grows. Typically you would compute them as follows: int dimx. Even for your GPU, the kernel runtime for peak performance would be only about 0.17ms (or 0.23ms). Additionally every thread has only 10 double precision operations (14 if a is calculated twice for a*a) which results in a total of 2.5 MFLOP (or 3.5 MFLOP). You are launching kernels with a grid size of only 16x16 blocks or ~250k threads. Then, we would have had 1D blocks within a 2D grid dim3 dimBlock(ceil(W/256.0). WorkloadĪnother problem is the small problem size. Suppose we had fixed the number of threads in a block to be 256 in 1 dimension. This results in a peak performance of about 15 GFLOP/s only (~0.95GHz). Two suggestions why your runtime is so long: HardwareĪs you want to do the calculations with double precision you should look out for hardware that provides many more double precision units. The GPU device I am using is NVidia Quadro K2000, and my CUDA version is 7.5.

So please consider that the data have sorted to optimize the memory access.Īll the calculations must be done with double precision to decrease the round off errors. I have sorted the data in the original code before using them in kernel which I think it has a positive effect on future memory accesses. Std::cout << "KERNEL TIME = " << distanceCheck << " milliseconds" << std::endl dim3 variables which give the grid dimensions and block dimensions.

Thrust::raw_pointer_cast(&particles2),ĬudaEventElapsedTime(&dummymili, start, stop) Calculate the column index of the Pd element, denote by x int x threadIdx.x +. A grid can have 1 to 65535 blocks, and a block (on most devices) can. GridSize.y = (particles2.size() + blockSize.y - 1) / blockSize.y A grid can contain up to 3 dimensions of blocks, and a block can contain up to 3 dimensions of threads. GridSize.x = (particles1.size() + blockSize.x - 1) / blockSize.x

Thrust::device_vector distance(particles1.size()*particles2.size(), true) ĭim3 blockSize(32,32) // also tested for blockSize(16,16) The number of thread blocks in a grid is usually dictated by the size of the data being processed, which typically exceeds the number of processors in the system. Thrust::device_vector d_xPos(h_xPos), d_yPos(h_yPos), d_zPos(h_zPos), d_h(h_h) void cenergy(float energygrid, dim3 grid, float gridspacing, float z. The kernel calculates the distance between several spatial points and based on whether they are neighbors or not, it fills a boolean vector.įor (int i = 0 i h_xPos(num), h_yPos(num), h_zPos(num), h_h(num,0.001) Calculate initial electrostatic potential map around the simulated structure. I have a kernel function which is consuming more than 70% of execution time.

Dim3 grid calculation how to#

If someone knows how to solve this problem, would be very helpful.I am developing a CUDA program and I want to enhance my performance. "Invalid _global_ read of size 4" for the * *AA * C ] line. "Program hit cudaErrorInvalidValue (error 1) due to "invalid argument" on CUDA API call to cudaMemcpy." In this particular case, I have to use a double pointer for calculation. For the matrix, I have used a double-pointer. I'm trying to do a Matrix*vector*vector calculation using CUDA with C++. Dimensions of the grid in blocks (gridDim.z not used) Dimensions of a thread block in threads dim3 blockIdx, threadIdx Block index within the grid Thread index within the block global void KernelFunc(.