D&I of GPU based Image Processing on CASE cluster

7/29/2019 D&I of GPU based Image Processing on CASE cluster

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Presented By:

1. Usama Aziz E09-108

2. Raania Naeem Khan E09-083

Supervised by:

Dr. M.Shamim Baig


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Introduction

Computing at a Glance

Need for GPU computing

History of GPU computing

Transition to GP-GPUs Utilizing GPUs for general purpose computing

Clusters

Our Cluster.

Cluster Specifications. Our Graphic Card

Cluster Working

Applications

Project Timeline

Content


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Introduction


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objectives

Designing & Implementing a General Purpose GPU based clustercomputing system comprising of 1 master & 8 computer nodes

capable of executing an image processing system in a cost effective

and power efficient manner.


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1980-1999 2000-2005


Processing powerincreased byincreasing clockrate

Desktop1MHz

Desktop1GHz

x 1000

Limitations (processing power):-IC fabrication-power-heat-rapidly approaching limit totransistor size

Era of Multicorecomputing

MulticoreCPUsavailable tocustomers

2005- onwards

Era of GPUComputing


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Low power consumption

Considerable Low cost

Greater computational power

Why GPUs?


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Motivation: Computational Power


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GPU-Computing: Concepts and Constraints

Conventionally GPUs produced a color against every pixelusing input colors, spatial and texture coordinates

Input colors could represent any data hence GPUs

could be tricked to perform multiple tasks.

GPUs usage as general purpose processor requiredgraphics-only languages

Nov 2006 NVIDIA first GPU GeForce 8800 GTX onCUDA Architecture was launched to addressconstraints.


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Compute Unified DeviceArchitecture :CUDA

Allows every ALU to be accessed while performinggeneral purpose task

Capable of handling floating point operations

Introduction of CUDA C (2007)harnessed features of

the CUDA architecture


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Overview of GPU computing

3D Graphic

concepts in

cinematography

by Silicon

Graphics

2D Graphics

Rise in Poplularityamong

consumers due to

incorporation in

Windows

1992- OpenGL

libraries to assist 3D graphicapplications

1995 onwards

-rise in demand of consumer

graphics games (Doom/Duke

Nukem)

-NVIDIA graphic accelerators

GeForce 3 series

Direct X 8.0standard (1st time

user controlled

GPU computations)

NVIDIA- Fermi

Architecture forGPUs 2DP

GFLOPs/Watt

Graphic Accelerators

Hardware assisted

bitmap operations to

assist display & usability

sold as part of CPUs and

speeding up graphics

NVIDIA GeForce

256

Transform & lighting

computations

directly on Graphics

Processor (part Open

GL graphics pipeline)

NVIDIA- Tesla

(2007)

Architecture for

GPUs < 2DP

GFLOPs/Watt

200920011991-20001980-1990

NVIDIA Kepler

(2011)

>5 DPGFLOPs/

Watt


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Content Introduction




Transition to GP-GPUs

Utilizing GPUs for general purpose computing

Clusters.

Our Cluster.

Cluster Specifications.

Our Graphic Card

Cluster Working

Applications

Project Timeline


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Group of computers and servers connectedtogether that act like a single system

Each system called a Node

Each Node contains one or more processor, RAM,HD, LAN card

Nodes operate in parallel. Performance can beincreased by adding more Nodes

Clusters


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Our Cluster


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Communication : Ethernet Switch 1GBSpecs:

-Master Node

Quad core i5 Processor 2.8 GHz

Ram 4GB

Hard disk 250GBGraphics Card NVIDIA GeForce GT210

-Node (1-8)

Quad core i5 Processor 2.8 GHz

Ram 4GB

Hard disk 250GB

Graphics Card NVIDIA GeForce GT210

-OS

Linux Cent-OS

-Cluster Management tool

Rocks(6.0) www.rocksclusters.org

Cluster specifications


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GeForce GT 210 Specifications

Manufacturer: NVIDIASeries: GeForce GT 200GPU: GT218Core Clk: 489 MhzMemory Clk: 500 MhzFLOPS: 44.864 GFLOPSMax Power Draw: 30.5WattNoise Level: SilentMemory Type: DDR2Frame Buffer: 512, 1024 MBMemory Bus Type: 64bit


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Working

Running Program(sequential)


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Working



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Working



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Data sent

Data sent

Data sent

Running Program(Parallel)


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Working

Working

Working

Working



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Finished

Finished

Finished

Results

Results

Results

Get results



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Applications of GPU computing

Digital Biology Molecular

Dynamics

Video Imaging Weather & OceanModelling

Forensic s

ScientificComputation


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Introduction Computing at a Glance



Transition to GP-GPUs Utilizing GPUs for general purpose computing

Clusters

Our Cluster.

Cluster Specifications.

Our Graphic Card

Cluster Working

Applications

Project Timeline

Content


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Project Timeline

Equipment Acquisition

Cluster Establishment

Running Test App Parallelizing Test App

Implementing Algorithm.


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References CUDA by example by Jason Sanders, Edward Kandrot

Programming Massively Parallel Processors by Kirk, Hwu

CUDA application design and development by Farber

GPU computing Gems by Hwu

Rocks user guide & Rolls base user guide

CUDA C getting started guide for Windows

Image browsing, processing and clustering for participatory sensing: Lessons froma diet sense prototype Reddy, Parker, et al.

Parallel Image Processing System on a cluster of personal computers Barbosa,Travares et al.

NVIDIA Fermi Whitepaper

www.rocksclusters.org

www.wikipedia.org

www.nvidia.com
http://www.rocksclusters.org/http://www.wikipedia.org/http://www.nvidia.com/http://www.nvidia.com/http://www.wikipedia.org/http://www.rocksclusters.org/


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Thank you for your time.

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D&I of GPU based Image Processing on CASE cluster