cuda sdk

The Cuda 4.0 version is very different from the previous Cuda 2.3 version. At least, the Cubin format is changed to the ELF File and is no longer the decuda input file. I tested my graphics card with a GPU-Z, gt218 supports opencl, Cuda, directcompute4.1. All right, everything is installed, including vs2008. Below is a simple

Installation Process of CUDA (including GPU driver) in Ubuntu OS: Ubuntu 12.04 (amd64) Basic tool set Aptitude install binutils ia32-libs gcc make automake autoconf libtool g ++-4.6 gawk gfortran freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev-y If it is a server system without a graphical interface, the lightdm GUI manager step is not stopped... This stuff shouldn't be available on the serve

Original link1. OverviewThe data in the texture memory is stored in memory in the form of one-, two-, or three-dimensional arrays, which can be accessed through the cache and can be declared larger than the constant memory.The operation to access the texture memory in kernel is called texture pickup (texture fetching). The operation of associating data in memory with a texture reference frame is called binding the data to the texture (texture binding).There are two types of data that can be boun

Today, I thought about the Cuda zero-memory copy problem. I felt that it would be useful in the program to be designed, so I checked the relevant information. The following are some helpful links: Zero copy usage in Cuda -- for two-dimensional pointers Zero-copy usage in Cuda -- for one-dimensional pointers Cuda zero

Not much to say, experienced a lot of inexplicable errors finally installed, directly put the installation script:#!/bin/bash# installation should be aware that some libraries may fail to install and install Caffe and PROTOBUF related errors may need to re-make the ProtobufInstallCD/home/zw/Softwares #需要事先下载对应版本的cudasudoDpkg-i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.debsudoapt-Get updatesudoApt-getInstallCUDACD/home/zw/git_home/#我存放git项目的目录g

Cuda-convnet is a set of CNN code published by Alex Krizhevsky , running on a Linux system, using the GPU to perform operations, providing only a demo of the CIFAR data set in Cuda-convnet. And the website does not explain how the Cuda-convnet code is used in other databases, so Bo Master I try to modify the source, for the mnist data set, to do a handwritten num

Tags: code stat leave Tor dia pool ack drivers what to doBy TensorFlow 1.8, Ubuntu 16.04, Cuda 9.0, nvidia-390 tortured for 5 days, finally on the pit, leaving a guide for the benefit of posterity.1. Find out the dependencies first:TensorFlow 1.8 relies on Cuda 9.0,cuda 9.0 dependent nvidia-390.2. Pit:Only nvidia-384,nvidia-390 newness is not supported in Ubuntu

This is also troubled me for a long time, before using Http://www.cnblogs.com/platero/p/3993877.html installation method, installed 567,890 times, always a problem.Later found a new method, one night plus half the morning, installed the Ubuntu system (14.04) + NVIDIA driver + CUDA + CAFFE all done. Also ran the Mnist database, Shuangshuang a little problem. Specific steps:1. Install Ubuntu, it is recommended to install English language version (I inst

Find Visual Studio >> Visual Studio Tools in the Start menuChoose the 86 or 64 version of the VC command prompt environment that I useVS2013 x86 Native Tools Command PromptThis should configure the path of the VC compiler, and the path of the NVCC (cuda C compiler) in the environment variable.And then enterNVCC Cudafilename.cu-o OutfilenameThis format, such asNVCC Hello.cu-o HelloThe Hello.cu file is compiled, and the Hello.exe file is generatedBy the

vs2015+cuda8.0 Environment Configuration Anyway, record the correct configuration here: 1, first, the officer network download corresponding vs version of Cuda Toolkit: Https://developer.nvidia.com/cuda-toolkit-50-archive (Remember vs2010 corresponds to cuda5.0,vs2013 corresponds to cuda7.5,vs2015 corresponding to CUDA8.0) 2, then, the direct installation, remember in the installation process if you do not

Reprinted from: http://blog.sina.com.cn/s/blog_a43b3cf2010157ph.html There are several ways to write parallel programs that utilize GPU acceleration, which are summed up in three ways: 1. Take advantage of the existing GPU function library. Nvidia's Cuda Toolbox improves free GPU-accelerated fast Fourier transform (FFT), Basic linear algebra subroutines (BLAST), image and video processing library (NPP). The user can get performance acceleration by rep

Tags: tail command mil compilation 4.4 Ace ASI Add AliThis configuration is only a test configuration, it is estimated that with this type of graphics card to do parallel computing almost no, but the configuration method, a lot of it is worth borrowing, and ultimately want to apply in GEANT4 parallel computing.All right, ladies and gentlemen, I'm starting to play.First, if you have failed to install many times, then make sure that you have uninstalled the existing graphics drivers and Cuda1. If

architecture more perfect, more stable, more efficient, Many large development teams will find a way to solve these problems, and sun and other distributed leaders realize that in the near future each development team will create their own dedicated solutions, so the Java EE architecture is derived, in order to enable these development teams to quickly implement the above solutions, And the main focus on business processes, should almost understand it. Simply put, the JDK is a developer-orient

CUDA Driver API Usage notes1. IntroductionThe Cuda Driver API is implemented in the Cuda Dynamic Library (libcuda.so). If you are developing in an eclipse environment, you need to add the path to the libcuda.so file and reference the Cuda.h file in your program.2. Environment configuration2.1 Source ProgramFor the use of the driver API simply include the correspo

Installation InstructionsPlatform: Currently available on Ubuntu, Mac OS, WindowsVersion: GPU version, CPU version availableInstallation mode: PIP mode, Anaconda modeTips: Currently supports python3.5.x on Windows GPU version requires cuda8,cudnn5.1 Installation progress2017/3/4 Progress:Anaconda 4.3 (corresponding to python3.6) is being installed, deleted, nothing.2017/3/5 Progress:Anaconda 4.3 (corresponds to python3.6) getAnaconda in Python3.5.2getTensorflow1.0.0getIdeasIn t

Everybody put Gpucuda said very NB malicious NB, so, next want to run through the GPU Acceleration program. This one weeks, all in the configuration OpenCV cuda environment, today finally ended in failure, because the lab's machine graphics do not support cuda ... Can't hurt, a week AH!!!CUDA-enabled Gpu:http://developer.nvidia.com/

1. Two-D arrays using1#include 2#include 3 using namespacestd;4 5 Static Const intROW =Ten;6 Static Const intCOL =5;7 8 intMain () {9 int* * Array = (int**)malloc(row*sizeof(int*));Ten int* Data = (int*)malloc(row*col*sizeof(int)); One A //Initialize the data - for(intI=0; i) { -Data[i] =i; the } - - //Initialize the array - for(intI=0; i) { +Array[i] = data + i*COL; - } + A //Output the array at for(intI=0; i) - for(intj=0; j)

Sometimes it is necessary to do coding work through Remote Desktop Connection, such as the general web, such as the need for the GPU and other support coding work directly with Windows Remote Desktop Connection coding and then debug, and some need to rely on graphics support work such as rendering, When GPU operations such as CUDA, Remote Desktop Connection debug will fail. Because when using Remote Desktop to connect computer B, such as the original

Introduction to Ubuntu 16.04 Development Cuda Program (i)Environment: Ubuntu 16.04+nvidia-smi 378.13+cmake 3.5.1+cuda 8.0+kdevelop 4.7.3 Environment ConfigurationNvidia driver, CMake, Cuda configuration method See: Ubuntu 16.04 Configuration Run kintinuous kdevelop configuration: command line input sudo apt-get install Reference DocumentsLiu Jinxian and so on. Pa

Caffe is a very clear and efficient deep learning framework, now has a lot of users, but also gradually formed their own community, the community can discuss related issues. I began to look at the relevant content of deep learning to be able to use Caffe training to test their own data, see a lot of sites, tutorials and blogs, also took a lot of detours, the whole process to comb and summarize, in order to expect can be easily through this article can be easy to use Caffe training their data, Ex