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Posts Tagged ‘GPU Computing’

Architectures of Mobile Cloud Computing

Posted by Hemprasad Y. Badgujar on August 30, 2014


“Mobile Cloud Computing at its simplest, refers to an infrastructure where both the data storage and the data processing happen outside of the mobile device. Mobile cloud applications move the computing power and data storage away from mobile phones and into the cloud, bringing applications and mobile computing to not just smartphone users but a much broader range of mobile subscribers”.

1
From the concept of MCC, the general architecture of MCC can be shown in Fig.  In Fig. , mobile devices are connected to the mobile networks via base stations (e.g., base transceiver station (BTS), access point, or satellite) that establish and control the connections (air links) and functional interfaces between the networks and mobile devices. Mobile users’ requests and information (e.g., ID and location) are transmitted to the central processors that are connected to servers providing mobile network services. Here, mobile network operators can provide services to mobile users as AAA (for authentication, authorization, and accounting) based on the home agent (HA) and subscribers’ data stored in databases. After that, the subscribers’ requests are delivered to a cloud through the Internet. In the cloud, cloud controllers
process the requests to provide mobile users with the corresponding cloud services. These services are Accepted in Wireless Communications and Mobile Computing -developed with the concepts of utility computing, virtualization, and service-oriented architecture (e.g.,web, application, and database servers).

http://onlinelibrary.wiley.com/doi/10.1002/wcm.1203/abstract

Posted in CLOUD, Computer Network & Security, Computer Software, Computing Technology | Tagged: , | Leave a Comment »

Installing CUDA Toolkit 5.5 on Ubuntu 12.10 Linux

Posted by Hemprasad Y. Badgujar on January 3, 2014


Installing CUDA Toolkit 5.5 on Ubuntu 12.10 Linux

The following explains how to install CUDA Toolkit 5.5 on 64-bit Ubuntu 12.10 Linux. I have tested it on a self-assembled desktop with AMD Phenom II X4 CPU, 4GB RAM, 500GB hard drive, 650W power supply, and NVIDIA GeForce GTX 550 Ti graphics card. The instruction assumes you have the necessary CUDA compatible hardware support. Depending on your system configuration, your mileage may vary.

 

CUDA Repository

Retrieve the CUDA repository package for Ubuntu 12.10 from the CUDA download site and install it in a terminal.

$ sudo dpkg –i cuda-repo-ubuntu1210_5.5-0_amd64.deb
$ sudo apt-get update

 

Linux Kernel Header

Then you need to install the necessary Linux kernel headers.

$ sudo apt-get install linux-headers-‘uname -r‘

 

Proprietary Video Driver

The built-in nouveau video driver in Ubuntu is incompatible with the CUDA Toolkit, and you have to replace it with the proprietary NVIDIA driver.

$ sudo apt-get remove –purge \
    xserver-xorg-video-nouveau

$ sudo apt-get install nvidia-settings \
    nvidia-current-dev nvidiamodprobe

You can reboot the system afterwards and verify the driver installation with the nvidia-settings utility.

 

CUDA Toolkit

Then you can install the CUDA Toolkit using apt-get.

$ sudo apt-get install cuda

 

Environment Variables

As part of the CUDA environment, you should add the following in the .bashrc file of your home folder.

export CUDA_HOME=/usr/local/cuda-5.5
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64

PATH=${CUDA_HOME}/bin:${PATH}
export PATH

 

CUDA SDK Samples

Now you can copy the SDK samples into your home directory, and proceed with the build process.

cuda-install-samples-5.5.sh  ~
cd ~/NVIDIA_CUDA-5.5_Samples
$ make

If everything goes well, you should be able to verify your CUDA installation by running thedeviceQuery sample in bin/linux/release.

Posted in Computer Languages, Computing Technology, CUDA, GPU (CUDA), PARALLEL | Tagged: , , , , , | Leave a Comment »

Installing CUDA 5 on Ubuntu 12.04

Posted by Hemprasad Y. Badgujar on January 3, 2014


This guide is based on Ubuntu 12.04 LTS, but the same principles apply in newer versions of Ubuntu. Let’s begin.

nvidia-cuda

1. Make sure you have a CUDA supported GPU

You must have a nVIDIA GPU that supports CUDA, otherwise you can’t program in CUDA code. Here’s a list with the CUDA supported GPU models.

2. Install nVIDIA proprietary drivers

Use Jockey (additional drivers) or just pick the driver you want from the NVIDIA official website.

3. Download CUDA Toolkit 5.0 for Ubuntu

I used the Ubuntu 11.10 32bit version. It’s the latest version so far, but it currently works fine. So please download.

4. Fix the libglut.so error

There will be an error when you’ll try to install the CUDA 5.0 examples. The driver is trying to find the libglut.so file and it doesn’t look for other versions, such as so.1so.2 etc.

First confirm that you have a libglut file:

$ sudo find /usr -name libglut\*

If you do, symlink that file to libglut.so.

For 64bit:

$ sudo ln -s /usr/lib/x86_64-linux-gnu/libglut.so.3 /usr/lib/libglut.so

For 32bit:

$ sudo ln -s /usr/lib/i386-linux-gnu/libglut.so.3 /usr/lib/libglut.so

5. Install the CUDA Toolkit and Samples

Press CTRL+ALT+F1 to open a shell — yeah, we’re going to do this in old (yet powerful) command-line way, but there’s no need to be afraid of the black and white terminal with a blinking cursor. After all you know what they say, once you go black…

5.1 Shutdown the all the graphics

Ubuntu uses LightDM, so you need to stop this service:

$ sudo service lightdm stop

5.2 Run the installer

Go to (using cd) the directory where you have the CUDA installer (a file with *.run extension) and type the following:

$ sudo chmod +x *.run
$ sudo ./*.run

Accept the License and install only the CUDA 5 Toolkit and the Samples. DO NOT install the drivers because we have already done that.

6. Enable the nvcc compiler

In order to compile CUDA code you have to use the nvcc compile. In that so you have to tweak some environment variables into your ~/.bashrc file:

For 32bit:

export PATH=$PATH:/usr/local/cuda-5.0/bin
export LD_LIBRARY_PATH=/usr/local/cuda-5.0/lib

For 64bit:

export PATH=$PATH:/usr/local/cuda-5.0/bin
export LD_LIBRARY_PATH=/usr/local/cuda-5.0/lib64:/lib

If you want to compile a CUDA file (*.cu extension) you can use the following command:

nvcc -o file file.cu

./file

Or use the NSight Eclipse Edition.

– See more at: http://www.unixmen.com/how-to-install-cuda-5-0-toolkit-in-ubuntu/#sthash.IScUBu1O.dpuf

Posted in Computer Softwares, Computing Technology, CUDA, GPU (CUDA), GPU Accelareted, Installation, PARALLEL, Project Related, Research Menu, UNIX OS | Tagged: , , , , , , | Leave a Comment »

GPU Acceleration In Multilayer Parallel User Authentication Schemes By Optimizing Hardware Use

Posted by Hemprasad Y. Badgujar on May 29, 2013


GPU Acceleration In Multilayer Parallel User Authentication Schemes By Optimizing Hardware Use

P0212 GPU Acceleration In Multilayer Parallel User Authentication Schemes By Optimizing Hardware Use
Hemprasad Badgujar (Hardsoft)

Posted in Mixed | Tagged: , , , , | 1 Comment »

 
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