gpu

问题 There are several ways to handle synchronization in Vulkan. This is how I understand it: Fences are GPU to CPU syncs. Semaphores are GPU to GPU syncs, they are used to sync queue submissions (on the same or different queues). Events are more general, reset and checked on both CPU and GPU. Barriers are used for synchronization inside a command buffer. In my case I have two command buffers. And I want the second command buffer to execute after the first one. submitInfo.pCommandBuffers =

问题 I need to execute some off-screen rendering program on AWS EC2 GPU instance with CentOS. However, while I found that Ubuntu is very easy to setup, I cannot let CentOS work properly. The goal is to run some essential utility/test tool on EC2 GPU instance (without screen or X client). In the following article, I will describe how the Ubuntu can be setup and how CentOS/Amazon Linux AMI fails. Ubuntu On ubuntu 12.04, everything works very smoothly. The EC2 environment I used are: Instance type:

问题 I tried to install tensorflow with the version of GPU , but always failed. Computer system: win 10 professional Graphics Card : Nvidia Quadro M2000M (I verify this card in Nvidia official website ,it can support CUDA ) My installation process : 1- install Rapid Environment Editor 2- install DXSDK_Jun10.exe 3- install cuda_8.0.61 \ patch 2 and CUDNN5.1 4- install Visual Studio 2015 community And then ,I test the example in VS2015. 5- install Anaconda 4.4(I also try the vision-4.2,but it is

问题 I am trying to make use of the GPU present in TI Jacinto 6 target. I have compiled the QT5 opengl examples. I am able to run the qt5 opengl examples on TI J6. Now, I want to know if the application running is GPU accelerated or not. How can I find out? 回答1: Qt doesn't magically GPU-accelerate the entire application. Widgets are not accelerated unless they derive from QOpenGLWidget. Qt Quick 2 is accelerated by default; there's an option of using the software renderer on hardware that doesn't

问题 I want to use only the pip version of tensorflow as in conda version if tensorflow-gpu gets error code runs on cpu which is undesirable. After installing cuda 10 and cudnn for ubuntu 18.0.4 when I import tensorflow it gives me the following error. PS: I purged everything related to cuda and Nvidia and then installed the new Cuda 10. Python 3.6.7 |Anaconda custom (64-bit)| (default, Oct 23 2018, 19:16:44) [GCC 7.3.0] on linux Type "help", "copyright", "credits" or "license" for more

问题 While trying to speed up a simple algorithm using the GPU with OpenCV, I noticed that on my machine (Ubuntu 12.10, NVidia 9800GT, Cuda 4.2.9, g++ 4.7.2) the GPU Version is actually slower than the CPU version. I tested with the following code. #include <opencv2/opencv.hpp> #include <opencv2/gpu/gpu.hpp> #include <chrono> #include <iostream> int main() { using namespace cv; using namespace std; Mat img1(512, 512, CV_32FC3, Scalar(0.1f, 0.2f, 0.3f)); Mat img2(128, 128, CV_32FC3, Scalar(0.2f, 0

问题 I have written a code on Nsight that compiles and can be executed but the first launch can't be completed. The strange thing is that when I run it in debug mode, it works perfectly but it is too slow. Here is the part of the code before entering the function that access the GPU (where i think there is an error I can't find) : void parallelAction (int * dataReturned, char * data, unsigned char * descBase, int range, int cardBase, int streamIdx) { size_t inputBytes = range*128*sizeof(unsigned

问题 I have CUDA compatible GPU (Nvidia GeForce 1060) in my system. While analyzing a bigger dataset, I often have to use pair plot function of the seaborn library, it consumes a lot of time. Is there a way I can run my entire notebook on GPU. I mean, apart from seaborn, I want to run all of my code on GPU, is it possible? I have watched a couple of videos on youtube that advises using numba python compiler and jit annotations, I want to know whether there is a generic setting that can be applied

问题 I'm working on a "fujitsu" machine. It has 2 GPUs installed: Quadro 2000 and Tesla C2075. The Quadro GPU has 1 GB RAM and Tesla GPU has 5GB of it. (I checked using the output of nvidia-smi -q). When I run nvidia-smi, the output shows 2 GPUs, but the Tesla ones display is shown as off. I'm running a memory intensive program and would like to use 5 GB of RAM available, but whenever I run a program, it seems to be using the Quadro GPU. Is there some way to use a particular GPU out of the 2 in a

问题 I am working in parallelise [this file][1] on GPU using [PTX file with matlab parallel.gpu.CUDAkernel][2]. My problem with [kron tensor product][3] is the following. My code should multiply two vectors kron(a,b) by multiplying each element of the first vector a=<32x1> by the all elements of the other vector b=<1x32> and the output vector size will be k<32x32>=a.*b . I tried to write it in C++ and it worked, as I only concern about summing all the elements of 2d array. I thought I can make it