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C # GPU general computing technology

GPU's parallel computing capability is higher than the CPU, so recently there are also a lot of projects using GPU appear in our field of view, on InfoQ saw this article about Accelerator-V2, it is a research project of Microsoft Research Institute. It needs to be registered before it can be downloaded. I feel that it is a good first step in accessing general GPU computing, So I downloaded it back. In the

View graphics card and GPU information in CentOS

bc00 [size = 128][Virtual] Expansion ROM at f8f80000 [disabled] [size = 512 K]Capabilities: [60] Power Management version 3Capabilities: [68] MSI: Enable-Count = 1/1 Maskable-64bit +Capabilities: [78] Express Endpoint, MSI 00Capabilities: [b4] Vendor Specific Information: Len = 14 Capabilities: [100] Virtual ChannelCapabilities: [128] Power Budgeting Capabilities: [600] Vendor Specific Information: ID = 0001 Rev = 1 Len = 024 Kernel driver in use: nvidiaKernel modules: nvidiafb, nvidia We ca

WIN10 (64-bit) installing the TensorFlow GPU

"Python 3.6 + tensorflow GPU 1.4.0 + CUDA 8.0 + CuDNN 6.0"There is no pycharm to install the Pycharm first.1, python:https://www.python.org/downloads/release/python-364/Pull to the bottom and select Windows x86-64 executable installer download.Note the Add Python 3.6 to path check box, and then select Install Now.2, TensorFlow GPU 1.4.0 in Pycharm settings--project interpreter to add the corresponding versi

Win10 + python3.6 + VSCode + tensorflow-gpu + keras + cuda8 + cuDN6N environment configuration, win10cudn6n

Win10 + python3.6 + VSCode + tensorflow-gpu + keras + cuda8 + cuDN6N environment configuration, win10cudn6n Preface: Before getting started, I knew almost nothing about python or tensorflow, so I took a lot of detours When configuring this environment, it took a whole week to complete the environment... However, the most annoying thing is that it is difficult to set up the environment. Because my laptop is low in configuration, the program provided by

Ubunut16.04 installation Theano+gpu

1. Update NVIDIA Graphics drivers?? After installing the system, first update the graphics driver in the System Update Manager, as  Click Apply Changes2. Installing Numpy,scipy,theanoPIP installation cansudo pip install 3. Installing Cuda7.5sudo apt-get install Nvidia-cuda-toolkit5. Configuration. Theanorc?? Generate Files sudo gedit ~/.theanorc (note Do not miss a point in front of Theano) and copy the following, and then save, where Cuda one of the content is the location of Cuda installed.??

Three-dimensional spatial analysis "turn" based on GPU acceleration

Three-dimensional spatial analysis based on GPU accelerationTags: supermap geographic information System GisitArticle: SyedWith the rapid development and popularization of three-dimensional GIS, three-dimensional spatial analysis technology has become the hotspot of GIS technology in the application of its practicability. In the face of the increasingly large-scale data processing situation, in order to meet the practical needs of GIS industry for thr

Theano (Deep learning Tool) uses GPU for accelerated configuration and use

Recently used Theano wrote the MLP and CNN program, because the training sample large, CPU speed so slow, and then found a computer with Naivid graphics card configuration using the GPU, encountered a lot of problems, recorded as follows:Platform Description:System: WindowsXPpython:2.7, it is recommended to use Python (x, y) directly, including the Theano required NumPy library, save your own configurationtheano:0.6cuda:3.01 DownloadsDownload Install

Typical six GPU Parallel Optimization Strategies

Preface How to optimize existing programs in parallel is the most important practical issue in GPU parallel programming technology. This article provides several optimization ideas to point out the path for parallel program optimization. Preparation before optimization First, we need to clarify the goal of Optimization-is it necessary to speed up the program twice? Or 10 times? 100 times? Maybe you will not think about it. Of course, the higher the im

How to Use GPU hardware acceleration for Android

1.Glossary GPU: Graphic Processing Unit (graphics processor) OpenGL: Open Graphic Library defines the specification of a cross-programming language and cross-platform programming interface. Different vendors have different implementation methods. It is mainly used for 3D image (two-dimensional) painting. Surfaceflinger:Dynamic library in Android that is responsible for surface overlay and hybrid operations Skia:2d graphics library in Android Libagl:A

Chrome enables GPU hardware acceleration

The following is a chrome user's usage tips, hoping to help readers. Here we will introduce the methods for enabling hardware acceleration and pre-rendering: Go to about: flags in the chrome address bar and pull down the page to find GPU accelerated compositing and GPU accelerated canvas 2D. enable these two items. Chrome 11 does not have the GPU accelerated c

Beware of GPU memory bandwidth !!

Beware of GPU memory bandwidth For personal use only, do not reprint, do not use for any commercial purposes. Some time ago, I wrote a series of post-process effect, including the motion blur, refraction, and scattering of screen spance. Most shader is very simple. It is nothing more than rendering a full screen quad to the screen. Generally, there are no more than 10 lines of PS Code, without any branch or loop commands. It can be run only after sm1.

How to realize Android mobile "flying in the sky"? Need a "ladder" like the GPU Turbo

Entertainment, mobile phone-hosted graphics operations are growing. Especially for the glory of the mobile phone brand for young people, users of large online games, AR/VR and other functions of the smoothness, clarity requirements are rising, but also hope that mobile phone prices as close as possible to the people. The scary technique is to honor the secret law of balance between the two needs.It's a scary technology. The "scientific name", called the GPU

Remote connection and running Opengl/cuda and other GPU program examples tutorial

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

Implementation of 2-D FFT algorithm--base 2 fast two-dimensional Fourier transform based on GPU

implementation of 2-D FFT algorithm--base 2 fast two-dimensional Fourier transform based on GPU The first one-dimensional FFT of the GPU implementation (FFT algorithm implementation-based on the GPU base 2 fast Fourier transform), and then I need to do a second-dimensional FFT, probably the following ideas. The first thing to look at is definitely the formula:

CentOS 7 builds Linux GPU server

Tags: download export linux led direct down logs PNG root1. CUDA Toolkit InstallationTo Https://developer.nvidia.com/cuda-gpus query GPU-supported CUDA versions:To Https://developer.nvidia.com/cuda-downloads, according to the operating system choose to download the appropriate CUDA toolkit version, download is a. run file, the download is completed with the root user directly run the file installation.After the installation is finished. Run:Nvidia-smi

Deng Jidong Column | The thing about machine learning (IV.): Alphago_ Artificial Intelligence based on GPU for machine learning cases

Directory 1. Introduction 1.1. Overview 1.2 Brief History of machine learning 1.3 Machine learning to change the world: a GPU-based machine learning example 1.3.1 Vision recognition based on depth neural network 1.3.2 Alphago 1.3.3 IBM Waston 1.4 Machine Learning Method classification and book organization 1.3.2 Alphago In the past few years, the Google DeepMind team has attracted the attention of the world with a series of heavyweight jobs. Prior to

How to use the mic and GPU in High-performance computing (HPC)

1. Installation of GPU Dirver Dirver Name: Nvidia-linux-x86_64-310.40.run Before installation, you need to change the operating system mode to text mode, and modify the/etc/inittab run level to 3. Under the appropriate directory, run./nvidia-linux-x86_64-310.40.run, start installation driver After the installation is complete, run Nvidia-smi–l,nvidia-smi–a and nvidia-smi-l can view the information on the GPU

Win10 under TensorFlow GPU Edition installation

Get ready:System environment: WINDOWS10 + Anaconda3 + pycharm(1) environment configuration:Open Anaconda Prompt, enter the Tsinghua warehouse image, so the update will be faster:Input:Conda config--add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/--set show_channel_ URLs YesAlso in Anaconda Prompt use Anaconda to create a python3.5 environment, the environment name is TensorFlow, enter the following command:Conda create-n TensorFlow python=3.5Run 开始菜单 ->Anaconda3—>Anaconda Na

Function image generator for GPU parallel computing in. net

is started, you can select the opencl computing platform and device. If multiple opencl platforms are installed, you can choose any one. Currently, this program does not support multi-video parallel technology (SLI and crossfire ). NVIDIA Cuda platform interface Example: AMD app platform interface Example: Intel opencl platform interface Example: Enter the equation to make full use of your imagination! Note: When using graphics card computing, it is best no

Paper Note: Sparse Matrix Format Selection with Multiclass SVM for SPMV on GPU

Original: Benatia, A., Ji, W., Wang, Y, Shi, F. (August). Sparse Matrix Format Selection with Multiclass SVM for SPMV on GPU. In Parallel processing (ICPP), 45th International Conference on (pp. 496-505). Ieee.SPMV (Sparse matrix-vector multiplication) refers to the operation of multiplying a sparse matrix with dense vectors. In the case of sparse matrices, dense matrices are not suitable for matrix multiplication because most of the computation and

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