gpu-programming

I am working with some GPU programs (using CUDA 4.1 and C), and sometimes (rarely) I have to kill the program midway using Ctrl+C to handle some exception. Earlier I tried using CudaDeviceReset() function, but this reply by talonmies displaced my trust in CudaDeviceReset() and hence I started handling such exceptions the Old-Fashioned way, that is 'computer restart'. As the project size grows, this method is becoming a headache. I would appreciate if anyone has come up with a better solution. harrism I think this question is more fundamental -- it is really an app design issue and not a CUDA

Is it possible to compile .cl file using NVIDIA's nvcc compiler?? I am trying to set up visual studio 2010 to code Opencl under CUDA platform. But when I select CUDA C/C++ Compiler to compile and build .cl file, it gives me errors like nvcc does not exist. What is the issue? You should be able to use nvcc to compile OpenCL codes. Normally, I would suggest using a filename extension of .c for a C-compliant code, and .cpp for a C++ compliant code(*), however nvcc has filename extension override options ( -x ... ) so that we can modify the behavior. Here is a worked example using CUDA 8.0.61,

Let me take the hardware with computation ability 1.3 as an example. 30 SMs are available. Then at most 240 blocks are able to be running at the same time(Considering the limit of register and shared memory, the restriction to the number of block may be much lower). Those blocks beyond 240 have to wait for available hardware resources. My question is when those blocks beyond 240 will be assigned to SMs. Once some blocks of the first 240 are completed? Or when all of the first 240 blocks are finished? I wrote such a piece of code. #include<stdio.h> #include<string.h> #include<cuda_runtime.h>

I'm trying to experiment with software defined radio concepts. From this article I've tried to implement a GPU-parallelism Discrete Fourier Transform. I'm pretty sure I could pre-calculate 90 degrees of the sin(i) cos(i) and then just flip and repeat rather than what I'm doing in this code and that that would speed it up. But so far, I don't even think I'm getting correct answers. An all-zeros input gives a 0 result as I'd expect, but all 0.5 as inputs gives 78.9985886f (I'd expect a 0 result in this case too). Basically, I'm just generally confused. I don't have any good input data and I don

I am trying to get started with CUDA programming on Windows using Visual Studio 2010 Express on a 64 bit Windows 7. It took me a while setting up the environment, and I just wrote my first program, helloWorld.cu :) Currently I am working with the following program: #include <stdio.h> __global__ void add(int a, int b, int *c){ *c = a + b; } int main(void){ int c; int *dev_c; HANDLE_ERROR( cudaMalloc( (void**)&dev_c, sizeof(int) ) ); add<<<1,1>>>(2, 7, dev_c); HANDLE_ERROR( cudaMemcpy( &c, dev_c, sizeof(int), cudaMemcpyDeviceToHost ) ); printf("2 + 7 = %d\n", c); cudaFree( dev_c ); return 0; }