cuda sdk

Nvidia DIGITS Learning Notes (nvidia DIGITS-2.0 + Ubuntu 14.04 + CUDA 7.0 + CuDNN 7.0 + Caffe 0.13.0)Enjoyyl 2015-09-02 machine learning original linkNVIDIA DIGITS-2.0 + Ubuntu 14.04 + CUDA 7.0 + CuDNN 7.0 + Caffe 0.13.0 Environment configuration Introduction Digits Introduction Digits characteristics Resource information Description Digits installation

Yesterday the two managers were working properly, updated today, found unavailable, ran AVD manager and SDK Manager did not respond , searched a lot of articles, and then saw the next article " about the SDK update Android SDK Tools 25.3.1 version after using SDK Manager Flash back "http://blog.csdn.net/ityangjun/arti

previous firegun, it takes a shot to charge a gun before it can be shot. Each access time is-clock (core clock) latency. In Cuda programming, memory access is one of the bottlenecks. The bandwidthtest provided by the SDK can be used to test the transmission performance from the host to the device, from the device to the host, and from the device to the device. Although PCIe has a theoretical value of 3.2 G

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

Based on years of Cuda development experience, we will briefly introduce the general development steps of the Cuda program, and follow the principle of first modifying the CPU serial program and then porting it to the GPU platform, modify the work that needs to be done on the GPU as much as possible on the CPU platform, reducing the difficulty of Program Development and debugging with bugs. By implementing

Install some dependencies first$sudo apt-get Install Freeglut3-dev build-essential libx11-dev libxmu-dev libgl1-mesa-dev Libglu1-mesa Libglu1-mesa-dev Libxi-devThere are two options for offline Installation: Debian installation package; Run Installation methodDeb Installation MethodThis is the case with Cuda 7.5 for 14.04, preferably MD5 validation of cuda installation files prior to this$ md5sum

of glupostredisplay () in idle and idle, the program returns to display again. At this time, the timer at the start of display record the current moment again. The difference between the two moments is the time used to render a frame. When the FPS is very high, you can accumulate time to obtain accurate FPS. For example, a frame may only be 0.000001 ms, but the time of 100000 frames is relatively large. In addition, the gluswapbuffers will call glfinish () implicitly, which is used in the

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

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

Since this book contains a lot of content, a lot of content is repeated with other books that explain cuda, so I only translate some key points. Time is money. Let's learn Cuda together. If any errors occur, please correct them. Since Chapter 1 and Chapter 2 do not have time to take a closer look, we will start from Chapter 3. I don't like being subject to people, so I don't need its header file. I will re

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

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