parallel computing.ppt
DESCRIPTION
Description of parallel computing and its different platforms.TRANSCRIPT
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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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