Santosh PandeyRam Sharan Chaulagain

Prakash Gyawali

SUPER COMPUTING USING CLUSTER

NETWORK- A supercomputer( HYPE -2 )

SupervisorProf. Dr. Subarna Shakya

“HIGH PERFORMANCE COMPUTING

IS THE NEED OF

FUTURE TECHNOLOGY”

THIS IS JUST THE BEGINNING

MOORE’S LAW

OUR OPTIONS:

MULTIPROCESSOR SYSTEM MULTICOMPUTER SYSTEM

PARALLEL PROCESSING

Speedup in multiprocessingDepends on parallelizable code

AMDAHL'S LAW

S(P)=Speedup on P processors

T(1)=Time to process in 1 processors

T(P)= Time to process in processors

f=Inherently sequential codep= Parallelizable code

High performance computing for researchAchieving super computing at a cheaper rate

than mainframes

OBJECTIVES

Muni Sakhya (1980’s)16 nodesFirst and the only one

History of Supercomputer in Nepal

Not An Application But Self designed High Performance Computing Architecure And

Cluster Network

MiddlewareNetwork ArchitectureMulticore Computers

Developing SuperComputing Framework

SYSTEM DESIGN

SIMD (Single Instruction Multiple Data)MIMD (Multiple Instruction Multiple Data)

Parallelism Supported By Architecture

Every application don’t have same parallelism model

Specific Applications must be programmedExtend Methods of our Architecture

Application Development

SYSTEM ARCHITECTURE

Computational Model

Dynamic Worker Addition and ReductionFault Tolerant Scalable System

FEATURES OF OUR ARCHITECTURE

Platform Independent

Network TopologyStar Topology

Parallel Working

METHODOLGY

Server Thread for each Worker at Server side

New Process for each Worker at Client side

Multitasking Model

SERVER

THREAD1

• Provide Chunk 1 To Client1

THREAD 2

• Provide Chunk 2 To Client2

THREAD N

• Provide Chunk N To Client N

Connect to

server

Take chunk to process

ProcessProvide

output to server

Connect to

server

Take chunk to process

ProcessProvide

output to server

Connect to

server

Take chunk to process

ProcessProvide

output to server

Running thousands of flops operationsIntegration for finding the value of Pi

Testing

RESULT AND ANALYSIS

RESULT AND ANALYSIS

1,41,3

2,13,1

3,23,3

4,3

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

Time (ms)

RESULT AND ANALYSIS(contd..)

1(10000) 2(20000) 5(50000) 7(70000) 15(150000)0

2

4

6

8

10

12

14

16

18

20

11.954

4.85.9

18

Speedup for 100 Million Iterations

No. of Nodes

Spee

dup

Fig : Exponential Speedup

RESULT AND ANALYSIS(contd..)

1(10000) 2(20000) 5(50000) 7(70000) 15(150000)0

2

4

6

8

10

12

14

16

18

20

11.954

4.85.9

18

Speedup for 100 Million Iterations

No. of Nodes

Spee

dup

Theory vs. Practical Data

No official data for comparing

Probably the fastest in Nepal

Comparing in Nepal

CryptographyData MiningWeather ForecastingResearchArtificial Intelligence

APPLICATION

Not comparable with bigger super computer due to less nodes

Extension of Architecture library to define new application

LIMITATIONS

Supporting Complex ComputationsInter-process Communication for dependent

tasksImplementing GPU for Computation

FUTURE IMPLEMENTATIONS

Websites: Don Berker. Robert G. Brown. Greg Lindahl. Forrest Hoffman.

Putchong Uthayopas. Kragen Sitaker. Frequently Asked Questions [Online]. Available: http://www.beowulf.org/overview.faq.html

Technopedia. Computer Cluster [Online]. Available: http://www.technopedia.com/definition/6581/computer-cluster

Dr. Wu-chun. Feng. (2015). The Green500 list- November 2015 [Online]. Available: http://www.green500/list/green201511

Books: Shiflet, Introduction to Computational Science: Modeling and

Simulation for Sciences, Princeton University Press, 2014. Kumar, Lenina, MATLAB: Easy Way to Learning, PHI Learning, 2016. Etter, Introduction to MATLAB, Prentice Hall, 2015 Lemay Laura, Charles L. Perkins, Teach Yourself Java in 21 Days,

Samsnet, 1996.

BIBLIOGRAPHY

THANK YOU