nvidia

I am tring to open an OpenGL full screen window using GLFW on linux red-hat. I have a desktop that spans two monitors with total resolution of 3840*1080. I have two problems: 1. The window is opened just on one monitor with maximum window width of 1920 (the width of a single monitor). 2. The maximum height of the window is 1003 (which I think is the height of the screen minus the height of the task bar and the top bar). This is the code I use to open the window: if (glfwInit() == GL_FALSE) std::cout<< "Unable to initialize GLFW\n"; glfwOpenWindowHint(GLFW_STEREO, GL_FALSE); if (glfwOpenWindow

Is it possible to compile OpenCL using Mingw and Nvidia SDK? I'm aware that its not officially supported but that just doesn't make sense. Aren't the libraries provided as a statically linked libraries? I mean once compiled with whatever compiler that may be, and linked successfully, whats should be the problem? I managed to compile and successfully link my code to OpenCL libraries provided with Nvidia's SDK, however the executable throws Segmentation Fault at clGetPlatformIDs which is the first OpenCL call in my code. Here is my compilation command x86_64-w64-mingw32-g++ -std=c++11 File.cpp \

Microsoft creates their windows and MFC DLL library, etc. An open source develop write a new MFC application and release the source code as GPL. The app has to link with the MS DLL/libraries to run in Windows, but I don't think anyone can argue that we now have the right to force the Microsoft's GPL their DLL. Does this mean the GPL license is really depends on which one is "created" first? If proprietary library is created first (such as Windows DLLs) that is published without linking and any GPL code and later a GPL program is linked with it, then the GPL program can't convert the

I have installed tensorflow using the anaconda environment as mentioned in the tensorflow website and after doing my python installation path changed. dennis@dennis-HP:~$ which python /home/dennis/anaconda2/bin/python And Jupyter was installed. I assumed that if I was able to import and use tensorflow in the conda environment that I will be able to do the same in Jupyter. But that was not the case - Importing tensorflow in my system (without activating the environment) dennis@dennis-HP:~$ python Python 2.7.11 |Anaconda 4.1.0 (64-bit)| (default, Jun 15 2016, 15:21:30) [GCC 4.4.7 20120313 (Red

The Situation I have a 2 gpu server (Ubuntu 12.04) where I switched a Tesla C1060 with a GTX 670. Than I installed CUDA 5.0 over the 4.2. Afterwards I compiled all examples execpt for simpleMPI without error. But when I run ./devicequery I get following error message: foo@bar-serv2:~/NVIDIA_CUDA-5.0_Samples/bin/linux/release$ ./deviceQuery ./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) cudaGetDeviceCount returned 38 -> no CUDA-capable device is detected What I have tried To solve this I tried all of the thinks recommended by CUDA-capable device , but

everybody, I have the following problem: I started a P2 instance with this AMI . I installed some tools like screen, torch, etc. Then I successfully run some experiments using GPU and I created an image of the instance, so that I can terminate it and run it again later. Later I started a new instance from the AMI I created before. Everything looked fine - screen, torch, my experiments were present on the system, but I couldn't run the same experiments as before: NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed

__global__ void helloCUDA(float f) { printf("Hello thread %d, f=%f\n", threadIdx.x, f); } int main() { helloCUDA<<<1, 5>>>(1.2345f); cudaDeviceSynchronize(); return 0; } Why is cudaDeviceSynchronize(); at many places for example here it is not required after kernel call? A kernel launch is asynchronous . This means it returns control to the CPU thread immediately after starting up the GPU process, before the kernel has finished executing. So what is the next thing in the CPU thread here? Application exit. At application exit, it's ability to send output to the standard output is terminated by

One thing I haven't figured out and google isn't helping me, is why is it possible to have bank conflicts with shared memory, but not in global memory? Can there be bank conflicts with registers? UPDATE Wow I really appreciate the two answers from Tibbit and Grizzly. It seems that I can only give a green check mark to one answer though. I am newish to stack overflow. I guess I have to pick one answer as the best. Can I do something to say thank you to the answer I don't give a green check to? Short Answer: There are no bank conflicts in either global memory or in registers. Explanation: The