gpu-constant-memory

I have some functions that load a variable in constant device memory and launch a kernel function. I noticed that the first time that one function load a variable in constant memory takes 0.6 seconds but the next loads on constant memory are very fast(0.0008 seconds). This behaviour occours regardless of which function is the first in the main. Below an example code: __constant__ double res1; __global__kernel1(...) {...} void function1() { double resHost = 255 / ((double) size); CUDA_CHECK_RETURN(cudaMemcpyToSymbol(res1, &resHost, sizeof(double))); //prepare and launch kernel } __constant__

I have the following code to copy from a host variable to a __constant__ variable in CUDA int main(int argc, char **argv){ int exit_code; if (argc < 4) { std::cout << "Usage: \n " << argv[0] << " <input> <output> <nColors>" << std::endl; return 1; } Color *h_input; int h_rows, h_cols; timer1.Start(); exit_code = readText2RGB(argv[1], &h_input, &h_rows, &h_cols); timer1.Stop(); std::cout << "Reading: " << timer1.Elapsed() << std::endl; if (exit_code != SUCCESS){ std::cout << "Error trying to read file." << std::endl; return FAILURE; } CpuTimer timer1; GpuTimer timer2; float timeStep2 = 0,

Does the use of data storage in constant memory provides any benefit over texture in the Pascal architecture if the data request frequency is very high among threads (every thread pick at least one data from a specific column)? EDIT: This is a split version of this question to improve community searching If the expectations for constant memory usage are satisfied, the use of constant memory is a good idea in the general case. It is allowing your code to take advantage of an additional cache mechanism provided by the GPU hardware, and in so doing putting less pressure on the usage of texture by

I'm trying to set my simulation params in constant memory but without luck (CUDA.NET). cudaMemcpyToSymbol function returns cudaErrorInvalidSymbol. The first parameter in cudaMemcpyToSymbol is string... Is it symbol name? actualy I don't understand how it could be resolved. Any help appreciated. //init, load .cubin float[] arr = new float[1]; arr[0] = 0.0f; int size = Marshal.SizeOf(arr[0]) * arr.Length; IntPtr ptr = Marshal.AllocHGlobal(size); Marshal.Copy(arr, 0, ptr, arr.Length); var error = CUDARuntime.cudaMemcpyToSymbol("param", ptr, 4, 0, cudaMemcpyKind.cudaMemcpyHostToDevice); my .cu

According to "CUDA C Programming Guide" , a constant memory access benefits only if a multiprocessor constant cache is hit (Section 5.3.2.4) 1 . Otherwise there can be even more memory requests for a half-warp than in case of the coalesced global memory read. So why the constant memory size is limited to 64 KB? One more question in order not to ask twice. As far as I understand, in the Fermi architecture the texture cache is combined with the L2 cache. Does texture usage still make sense or the global memory reads are cached in the same manner? 1 Constant Memory (Section 5.3.2.4) The constant

I am trying to understand resource usage for each of my CUDA threads for a hand-written kernel. I compiled my kernel.cu file to a kernel.o file with nvcc -arch=sm_20 -ptxas-options=-v and I got the following output (passed through c++filt ): ptxas info : Compiling entry function 'searchkernel(octree, int*, double, int, double*, double*, double*)' for 'sm_20' ptxas info : Function properties for searchkernel(octree, int*, double, int, double*, double*, double*) 72 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads ptxas info : Used 46 registers, 176 bytes cmem[0], 16 bytes cmem[14]