nvidia

I am new to Cuda, and I am trying to compile this simple test_1.cu file: #include <stdio.h> __global__ void kernel(void) { } int main (void) { kernel<<<1,1>>>(); printf( "Hello, World!\n"); return 0; } using this: nvcc test_1.cu The output I get is: In file included from /usr/local/cuda/bin/../include/cuda_runtime.h:59:0, from <command-line>:0: /usr/local/cuda/bin/../include/host_config.h:82:2: error: #error -- unsupported GNU version! gcc 4.5 and up are not supported! my gcc --version: gcc (Ubuntu/Linaro 4.6.1-9ubuntu3) 4.6.1 Copyright (C) 2011 Free Software Foundation, Inc. This is free

It seems like 2 million floats should be no big deal, only 8MBs of 1GB of GPU RAM. I am able to allocate that much at times and sometimes more than that with no trouble. I get CL_OUT_OF_RESOURCES when I do a clEnqueueReadBuffer, which seems odd. Am I able to sniff out where the trouble really started? OpenCL shouldn't be failing like this at clEnqueueReadBuffer right? It should be when I allocated the data right? Is there some way to get more details than just the error code? It would be cool if I could see how much VRAM was allocated when OpenCL declared CL_OUT_OF_RESOURCES. Eric Towers Not

I had recently some trouble making my pc (ivybridge) use the onboard gpu (intel igpu HD4000) for normal screen display usage, while i run my CUDA programs for computations on the discrete Nvidia GT 640 i have on my machine. The problem was that under iGPU display, CUDA would be unable to spot the nvidia card , and the nvidia drivers would not load at all. Keep in mind that there are confirmed issues (mostly about concurrency) when using the nvidia windows drivers for display devices, and also want to use CUDA. Those issues can get overridden when you use the Intel gpu as display (thus loading

I have been working on getting an application that relies on TensorFlow to work as a docker container with nvidia-docker . I have compiled my application on top of the tensorflow/tensorflow:latest-gpu-py3 image. I run my docker container with the following command: sudo nvidia-docker run -d -p 9090:9090 -v /src/weights:/weights myname/myrepo:mylabel When looking at the logs through portainer I see the following: 2017-05-16 03:41:47.715682: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your

I'm trying to use SlimDX and DirectX10 or 11 to control the stereoization process on the nVidia 3D Vision Kit. Thanks to this question I've been able to make it work in DirectX 9. However, due to some missing methods I've been unable to make it work under DirectX 10 or 11. The algorithm goes like this: Render left eye image Render right eye image Create a texture able to contain them both PLUS an extra row (so the texture size would be 2 * width, height + 1) Write this NV_STEREO_IMAGE_SIGNATURE value Render this texture on the screen My test code skips the first two steps, as I already have a