nvidia

笔记本联想Y450，Ubuntu 10.04.3 LTS 1.可以解决的问题： （1）安装驱动后开机和关机画面花屏，分辨率低； （2）开机画面直接出现，一闪而过，错过点点进度过程。 2.解决方案：uvesafb 替代 vesafb（具体什么意思我也不知道） 3.所需包： （1）v86d（uvesafb需要） （2）hwinfo（查看framebuffer） 4.步骤 （1）安装驱动： 方法一，Nvidia官网下载最新驱动，但是得先屏蔽掉nouveau，而且安装后会出现Nvidia巨大的Logo,我没有用 这种方法。 方法二，System-->Administration-->Hardware Drivers。 我使用的是第一种，不过有点问题，需要先禁用系统自带的 nouveau 步骤如下： 用官方驱动安装，将驱动拷到主文件夹，然后按Ctrl+Alt+F1退到终端并登录，sudo /etc/init.d/gdm stop，然后运行sudo sh ./Nv.......（驱动软件名），它会提示nouveau在使用，然后会问你是否建立一个文件来禁止nnouveau，选择Yes，一直点下去，会退出到终端界面，sudo reboot重新启动到GDM桌面，然后按Ctrl+Alt+F1退到终端，并登录，sudo /etc/init.d/gdm stop，退掉X桌面，再次运行sudo sh .

I am trying to use Yocto with meta-tegra ( https://github.com/madisongh/meta-tegra ) to build a minimal system for the Nvidia Jetson Nano. I need to use CUDA ( current version 10 for Nano ) with OpenCV on this platform. CUDA 10 only support GCC 7, and not GCC 8. GCC 7 has be deprecated and removed from OpenEmbedded Warrior release in favor of GCC 8.3. My error has to do with trying to use GCC 7 with Warrior release of OE: configure: error: cannot compute suffix of object files: cannot compile The README for meta-tegra states the following: * CUDA 10 supports up through gcc 7 only, and some

When I go to /usr/local/cuda/samples/1_Utilities/deviceQuery and execute moose@pc09 /usr/local/cuda/samples/1_Utilities/deviceQuery $ sudo make clean rm -f deviceQuery deviceQuery.o rm -rf ../../bin/x86_64/linux/release/deviceQuery moose@pc09 /usr/local/cuda/samples/1_Utilities/deviceQuery $ sudo make "/usr/local/cuda-7.0"/bin/nvcc -ccbin g++ -I../../common/inc -m64 -gencode arch=compute_20,code=sm_20 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch

I have a OpenCL program which runs fine for small problems but when running larger problems exceeds the 8-10s time limit for running kernels on Nvidia hardware. Although I have no monitors attached to the GPU I am computing on (Nvidia GTX580), the kernel will always be terminated once it runs for around 8-10s. The preliminary research I did on this problem indicates that the Nvidia watchdog should only enforce the time limit if a monitor is connected to the graphics card. However I do not have any monitors connected to the GPU the OpenCl is running on yet this limit is still enforced. Is it

Linux (Debian Sid x64), kernel 4.14, Nvidia GPU. I am unable to run Android emulator on open Nouveau drivers. There is no any error message that I can post, jus segmentation fault. When I choose software rendering, it works but unusable (it runs very slow). Does anybody know any workaround for that, or I am forced to use official Nvidia drivers? 来源： https://stackoverflow.com/questions/47900233/linux-run-android-emulator-on-nouveau-driver

When I try to run this simple OpenGL test program I get a segmentation fault. This only happens when I create the context using the core profile flag. If I use the compatibility profile flag, the program runs without issue. Edit: I checked the pointer to the function glGenVertexArrays and it returned NULL . If glfwCreateWindow doesn't return NULL , and glGetString(GL_VERSION) confirms that the context is version 4.3 and glewInit returns GLEW_OK then why is glGenVertexArrays == NULL ? My OS is Windows 7 64-bit and my GPU is a Nvidia GTX 760 with 331.82 WHQL driver. Code: #include <GL/glew.h>

I'm playing around with nvidia's unroll loops directive, but haven't seen a way to turn it on selectively. Lets say I have this... void testUnroll() { #pragma optionNV(unroll all) for (...) ... } void testNoUnroll() { for (...) ... } Here, I'm assuming both loops end up being unrolled. To stop this I think the solution will involve resetting the directive after the block I want affected, for example: #pragma optionNV(unroll all) for (...) ... #pragma optionNV(unroll default) //?? However I don't know the keyword to reset the unroll behaviour to the initial/default setting. How can this be done

I am learning CUDA with trying to solve some standard problems. As a example, I am solving the diffusion equation in two dimensions with the following code. But my results are different than the standard results and I am not able to figure that out. //kernel definition __global__ void diffusionSolver(double* A, double * old,int n_x,int n_y) { int i = blockIdx.x * blockDim.x + threadIdx.x; int j = blockIdx.y * blockDim.y + threadIdx.y; if(i*(n_x-i-1)*j*(n_y-j-1)!=0) A[i+n_y*j] = A[i+n_y*j] + (old[i-1+n_y*j]+old[i+1+n_y*j]+ old[i+(j-1)*n_y]+old[i+(j+1)*n_y] -4*old[i+n_y*j])/40; } int main() {