cuda header

use HARRISRESPONSES_GPU to get Harris response Cv::gpu::orb_gpu::computedescriptors (gpumat descriptors) CV:: Gpu::orb_gpu::mergekeypoints (gpumat keypoints) C, to determine the content of the function, the next step is to create a new project to perform the code we posted from the OpenCV, where there is a namespace conflict problem, my approach is to define the Orb algorithm namespace Cv::gpu replaced by the LMW, and the name of the class replaced by the Orb_ GPU0. D, the code to copy

Part of the content is transferred fromHttps://chenrudan.github.io/blog/2015/07/22/cudastream.htmlHttp://stackoverflow.com/questions/10415204/how-to-create-a-cuda-contextEarly on, it was discovered that the first function that was run on Cuda would take a long time to explain because Cuda initialization.So what's the main problem with

Summary of accelerated installation of Amber11 + AmberTools1.5 + CUDA The following installation method is based on some of the previous posts on the Forum simulated by the numerator. The installation and testing can be successful as long as the operation is correct. Considering that Amber11 is generally installed on clusters, the intel compiler and Openmpi parallel tool are used for installation. You need to purchase the Amber11 software to obtain th

First install Cuda:Download from the NVIDIA official website: Cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb, there are two types of run and Deb, heavily recommended Deb format, easy to installCD to the directory where Cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb is located, such as mine:CD ~/software/cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.debPerform:sud

This article is originally contained in my homepage:planckscale.info, reproduced here. Copyright Notice: Original works, welcome reprint, but reproduced please indicate the source of the article (Planckscale.info), author information and this statement in the form of hyperlinks, otherwise the legal liability will be investigated.In the previous article, two points to Cuda's computational power are very significant: data parallelism, and the use of multithreading to mask the delay. Next we'll go

Bo Master due to the needs of the work, began to learn the GPU above the programming, mainly related to the GPU based on the depth of knowledge, in view of the previous did not contact GPU programming, so here specifically to learn the GPU above programming. Have like-minded small partners, welcome to exchange and study, my email: caijinping220@gmail.com. Using the Geforce 103m graphics card on his old notebook, although the graphics card is already very weak relative to the current mainstream s

First verify that you have an NVIDIA graphics card (Http://developer.nvidia.com/cuda-gpus this site to see if you have a graphics card that supports GPU): $ LSPCI | Grep-i nvidia See your Linux distributions (mostly 64-bit or 32-bit): $ uname-m cat/etc/*release Look at the version of GCC: $ gcc--versionFirst download the NVIDIA Cuda Warehouse installation package (my Ubuntu 14.04 64 bit, so the down

Install Torch in Ubuntu and configure CUDA and cuDNNGeneral description Ubuntu is 14.04, and cuda is 7.5 cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64. Cudnn is 7.5, cudnn-7.5-linux-x64-v5.0-ga.tgz.Reference: Link: https://github.com/jcjohnson/neural-style/blob/master/INSTALL.mdNeural-styleIn fact, this article has clearly explained how to install it, but it still

1. Installing Build-essentialsInstall some basic packages needed for developmentInstall Build-essential2. Install the Nvidia driver (3.4.0) 2.1 Preparation work (2014-12-03 Update)In the case of shutting down the desktop management LIGHTDM, installing the driver seems to implement Intel HD graphics to display + NVIDIA graphics card to calculate. The steps are as follows:1. First select the Intel graphics card to display or use as the primary display device in the BIOS setup.2. Enter Ubuntu, pres

The author took a long time to install, mainly Cuda installation and OpenCV installation more laborious, Cuda find 32-bit installation package had to reinstall 64-bit Ubuntu system, OpenCV is also trying to solve, it is recommended to use 2.4.9 version. In fact, if the GPU does not need to install CUDA, but for subsequent compatibility considerations, the system

recompile. Three: Error Lookup In the CUDA, there is a good error-checking function, I generally use two of them: Cut_check_error (), Cuda_safe_call (), the former can accept the most recent cudaerror_t exception, and the output of the corresponding error type, For example, to detect if a kernel function executes correctly, you can add: Cut_check_error ("Kernel execution failed") after executing the statement, and if the runtime fails, the error type

The same machine can be compatible with multiple versions of the CUDA, these two days will be so back and forth, slightly trouble. 1. View the current Cuda version; NVCC--versionFrom this we can see that the current is Cuda 8.0. 2. Modify the. bashrc file. Export path= $PATH:/usr/local/cuda-8.0/bin export ld_library

Because of the project needs, our deep learning algorithm must be accelerated, so the group gave me two gpu:gtx-750 Ti GRID-K2 GTX-750 Ti was I installed in the local, GRID-K2 installed on the server, need to SSH login to use, followed by a variety of pits ......... ..... First, let's talk about Grid-k2, server-side installation: 1. First, if you have only this card, sorry, you can not click here to see Cuda supported GPU here to find the information

There are two versions that developers need to care about when developing Cuda applications: computing capability-describe product specifications and computing device features and Cuda driver API version-Describe the features supported by the driver API and runtime.You can obtain the driver API version from the macro cuda_version in the driver header file. Develo

Deep learning is an important tool for the study of computer vision, especially in the field of image classification and recognition, which has epoch-making significance. Now there are many deep learning frameworks, and Caffe is one of the more common ones. This article describes the basic steps for configuring Caffe in the Ubuntu 14.04 (64-bit) system, referring to the official website of Caffe http://caffe.berkeleyvision.org/.First, the system environment configuration1.1 First install some de

First, the Mac frame header definition/* Data frame definition, first 14 bytes, tail 4 bytes */typedef struct _MAC_FRAME_HEADER {Char m_cdstmacaddress[6]; Destination MAC AddressChar m_csrcmacaddress[6]; SOURCE MAC AddressShort M_ctype; The previous layer protocol type, such as 0x0800 represents the previous layer is the IP protocol, 0x0806 is ARP}__ATTRIBUTE__ ((packed)) Mac_frame_header,*pmac_frame_header;typedef struct _MAC_FRAME_TAIL {unsigned int

This article is originally contained in my homepage:planckscale.info, reproduced here. Copyright Notice: Original works, welcome reprint, but reproduced please indicate the source of the article (Planckscale.info), author information and this statement in the form of hyperlinks, otherwise the legal liability will be investigated.The previous article discussed how concepts such as blocks in the programming model map to hardware execution, and how Cuda

In the past two days, I took some time to study the problem of calling the Cuda program using Matlab. I found that there was less information on the Internet and the White Paper provided by NVIDIA was not detailed enough. Therefore, I would like to summarize the development process, hope you can use it. In general, there are two methods to call the Cuda program in MATLAB. The first is to create the DLL of t

Solution to installation failure of cuda-8.0 driver on centos7 Once upon a time, the NVIDIA Titan X card is inserted on the centos7 machine, according to the official website tutorial (https://developer.nvidia.com/cuda-downloads) one-click installation of cuda-8.0 everything went smoothly Installation Instructions: 'Sudo rpm-icuda-repo-rhel7-8-0-local-8.0.44-1.

Caffe + Ubuntu 15.04 + CUDA 7.5 Novice Installation Configuration GuideSpecial:0. Caffe website address: http://caffe.berkeleyvision.org/1. This article is for the author to complete the experiment, but only for the use of academic exchange, the use of this guide any adverse consequences of the user's own responsibility, not related to the author of this article, thank you! In order to ensure timely updates, reproduced please indicate the source, than