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SRMSWCET, BAREILLY

STUDY ON PARALLELCOMPUTING

( GUO-LIANG CHEN, GUANG -ZHONG SUN,YUN-

QUAN ZHANG AND ZE-YAOMO)

PRESENTED TO - PRESENTED BY-

MR. ROHIT JOHRI AYUSHI GAUR

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CONTENTS

INTRODUCTION

BASIC CONCEPTS

USES OF PARALLEL COMPUTINGPARALLEL COMPUTER ARCHITECTURE

PARALLEL PROGRAMMING MODELS

DESIGNING PARALLEL PROGRAMS

APPLICATIONS

CONCLUSION

REFERENCES

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INTRODUCTION

In the simplest sense, parallel com pu tingis

the simultaneous use of multiple compute

resources to solve a computational problem.

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REAL WORLD IS MASSIVELY

PARALLEL

parallel computing is much better suited for

modeling, simulating and understanding complex,

real world phenomena.

In the natural world, many complex, events are

happening at the same time.

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USES OF PARALLEL

COMPUTING

SCIENCE AND

ENGINEERINGAtmosphere, Earth, Environment

Physics

Biology

Chemistry

Geology, SeismologyMechanical Engineering

Electrical Engineering

Computer Science

Defense

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INDUSTRIAL AND COMMERCIAL

Databases, data mining

Web search engines

Medical imaging

Pharmaceutical design

Financial and economic modeling

Management of national and multi-nationalcorporations

Advanced graphics and virtual reality

Collaborative work environments.

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WHY USE PARALLEL

COMPUTING

Save time and/or money

Solve larger problems

Provide concurrency

Use of resources

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HARDWARE PLATFORM OF

PARALLEL COMPUTING

COMMON PARALLEL COMPUTER

ARCHITECTURE

SUPERCOMPUTER ARCHITECTURE

SHARED MEMORY

DISTRIBUTED MEMORY

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COMMOM PARALLEL

ARCHITECTURE

Cheap and Convenient.

Three type of Parallel Computers based on this

architecture :

Desktop Multiprocessors

Symmetric Multiprocessor

Cluster of Workstation

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SUPERCOMPUTERARCHITECTURE

High performance & high Cost

Main architectures are :

oMPP

oPVPoDSM

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SHARED MEMORY

Shared memory parallel computers vary widely.

Multiple processors can operate independently

but share the same memory resources.

Historically, shared memory machines have been

classified as UMA and NUMA , based upon memory

access times

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DISTRIBUTED MEMORY

Like shared memory systems, distributed memory

systems vary widely but share a common

characteristic.

Processors have their own local memory. Memory

addresses in one processor do not map to another

processor.

Because each processor has its own local memory,

it operates independently.

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THEORETICALBASEOFPARALLEL

COMPUTING: PARALLELALGORITHMS

PARALLEL COMPUTATION

MODEL :First generation parallel computation

model

Second generation parallel

computation modelThird generation parallel computation

model

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DESIGN AND IMPLEMENTATION

OF PARALLEL ALGORITHMS :

Policy

Method

Implementation

PERFORMANCE EVALUTION OF

PARALLEL ALGORITHM :

Speed Up

Efficiency

Scalability

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SOFTWARESUPPORTOFPARALLELCOMPUTING: PARALLEL

PROGRAMMING

PARALLEL PROGRAMMING

MODELS:

Shared Variable

Message Passing

Unified Model

PARALLEL BENCHMARK

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PARALLEL APPLICATIONS AND

ENABLING TECHNOLOGIES

Computational fluid dynamics (CFD)

Particle Transportation

Other Applications

Environment and Energy

Chemistry

Biology

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CONCLUSION

Parallel computing is fast.

There are many different approaches and models

of parallel computing.

Parallel computing is the future of computing.

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