enable cuda

Caffe is an efficient, deep learning framework. It can be executed either on the CPU or on the GPU.The following is an introduction to the Caffe configuration compilation process on Ubuntu without Cuda:1. Install the blas:$ sudo apt-get install Libatlas-base-dev2. Install dependencies: $ sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-devlibopencv-dev Libboost-all-dev libhdf5-se Rial-dev Protobuf-compiler Liblmdb-dev3. Install Glog (down

Preface: Today may be a relatively bad day, from the first phone in the morning, to the afternoon some of the things, some Xu lost. Sometimes really want to work and life completely separate, but who can really split so open, Mashikamu! A lot of times want to give life some definition, add some comments. But life is inherently a code that doesn't need to be annotated. Explain with 0来? Or is it an explanation? 0, the beginning of Heaven and earth, 1, the source of all things. Who can say clearly,

Let's take a look at the GPU test sample for the OPENCV background modeling algorithm: #include OPENCV provides some basic processing of CUDA programming, such as copying images from CPU to GPU (Mat-to-Gpumat), Upload,download. OPENCV encapsulation and shielding the cuda underlying functions, this has the advantage and disadvantage, for some people interested in algorithmic applications, very good, as lo

Writing is not necessarily right. Wrong, please. Preface This article is made of c++11 thread.Compiling is probably g++ Sort.cpp-o3-pthread-std=c++14-o Sort Actually, I haven't learned c++14. Recently began to learn Cuda, feel thread specific how to use and hardware is directly related to different architectures AH different precision AH the way to use threads should be different. This blog is an experiment, using CPU to achieve parity sorting for fu

the GPU, parallel computing, all of a sudden, we have a lot closer to the parallel computation. Now in school to learn the computer is from the serial algorithm began, formed a lot of fixed serial thinking. When the problem is divided in parallel, there is a serial of ideas, it is not good: Text: We have talked about some concepts of threads before, but these concepts are soft links. We often hear so-and-so units say how good their hardware and software configuration is. The software is good,

The problem occurs when you re-make the Caffe:sudo make runtest after reinstalling the video driver: Check Failed:error = = cudasuccess (30vs.0) unkown error ... 1. Uninstall the original Cuda sudo/usr/local/cuda-8.0/bin/uninstall-cuda-8.0.pl 2. Re-install Cuda 3. Problems occurred: /USR/BIN/LD:-lglut collect2 not foun

In the fifth lecture, we studied the GPU three important basic parallel algorithms: Reduce, Scan and histogram, and analyzed its function and serial parallel implementation method. In the sixth lecture, this paper takes the Bubble sort, merge sort, and sort in the sorting network, and Bitonic sort as an example, explains how to convert the serial parallel sorting method from the data structure class to the parallel sort, and attach the GPU implementation code.In the parallel method, we will cons

Original article link Today, we will introduce the relevant properties of Cuda devices. We can write code that is more suitable for hardware work only when we are familiar with the hardware and how it works. The cudadeviceprop struct records the properties of the device. 1 struct cudadeviceprop 2 {3 char name [256];/** Use cudagetdeviceproperties () to obtain the device attribute. Use cudagetdevicecount () to obtain the number of devices. Use cudacho

Source code and running result Cuda: https://github.com/zhxfl/cuCNN-I C language version reference from: http://eric-yuan.me/ The accuracy of the mnist library for famous handwritten numbers recognition is 99.7%. In a few minutes, CNN training can reach 99.60% accuracy. Parameter configuration The network configuration uses config.txt for configuration # comments between them, and the code will be filtered out automatically. For other formats, refer

Syntax highlighting in addition to the look comfortable, you can use F11 to find functions, variable definitions, hitting the function will also have a corresponding hint.The following is a set of code highlighting.In the Helloworldcuda.cu file above, the Cuda C + + keyword __global__ and so on are not highlighted, and there is a stroke curve. The following syntax highlighting of Cuda C + + keywords and fun

1. when compile /home/wangxiao/NVIDIA-CUDA-7.5 SAMPLES, it warning: gcc version larger than 4.9 not supported, so:old verson of gcc and g++ are needed: sudo apt-get install gcc-4.7 sudo apt-get install g++-4.7 Then, a link needed:sudo ln-S/Usr/Bin/gcc-4.7 / usr/local/cuda/bin/gccsudo ln - s /usr/bin /g++-4.7/usr/local/ cuda/bin/g ++ When c

What is CUDA Toolkit?For developers using C and C + + to develop GPU- accelerated applications, NVIDIA CUDA Toolkit provides a comprehensive development environment. CUDA Toolkit includes a compiler for Nvidia GPUs, many math libraries, and a variety of tools that you can use to debug and optimize application performance. You'll also find programming guides, use

0. IntroductionThis paper records the learning process of cuda-just beginning to touch the GPU-related things, including graphics, computing, parallel processing mode, first from the concept of things to start, and then combined with practice began to learn. Cuda feel no authoritative books, development tools change is faster, so the total feeling is not very practical. So this article is from the perspecti

(texref1d));//Unbind -Cutilsafecall (Cudafree (dev1d));//Free memory Space $ Cutilsafecall (Cudafree (DEVRET1D)); theFree (HOST1D);//free up memory space the Free (HOSTRET1D); the the ///2D Texture Memory -cout "2D Texture"Endl; in intwidth =5, height =3; the float*HOST2D = (float*) Calloc (width*height,sizeof(float));//Memory Raw Data the float*HOSTRET2D = (float*) Calloc (width*height,sizeof(float));//Memory return Data About theCudaarray *cuarray;//

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. Developers can check whether their applications req

Cuda file organization Original article address:Cuda Study Notes 2 Author:Ye Isaac Cuda file organization: 1. Cuda projects can contain. Cu AND. cpp. 2. In the. Cu file, you can use # include "cuda_x.cuh" to call the functions in. Cu or # include "cpp_x.h ". For example, declare Class A in test1.h; Define the related member functions of Class A in t

I have long heard of many advantages of Cuda nexus: Support for GPU thread debugging and analysis... It took me one afternoon to build the Cuda nexus environment. The following are the points to pay attention to when building: I. Hardware: During remote debugging, the target machine's video card must be a Cuda Device of G92 or gt200, and the host can be any vi

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

Compiling the environmentVMWare Workstation PlayerUbuntu-15.10-desktop-amd64CPU 4700MQ, allocating 6 cores +4GB memory +80GB HDD to VMCompile stepThe main reference is Caffe official websiteHttp://caffe.berkeleyvision.org/install_apt.html1. Install the Basic Package sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev Protobuf-compilersudo apt-get install--no-install-recommends Libboost-all-dev Cuda