slide scicom - r. m. ibrahim - 10208043

8/13/2019 Slide Scicom - R. M. Ibrahim - 10208043

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R. M. Ibrahim

Department of Physics

Institut Teknologi Bandung

PARALLEL COMPUTATION IMPLEMENTATION OF

CONJUGATE GRADIENT METHOD TO SOLVE THE

POISSONS EQUATION IN TWO-DIMENSIONAL

POTENTIAL DISTRIBUTION


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ParallelComputation

Basically 1 program for 1 computer

Algorithm parallelization

Utilize resource

Minimize time

Problem

What factors does affect parallel computation of

conjugate gradient performance?

INTRODUCTION


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CONJUGATE GRADIENT METHOD

Solve large Linear Equation SystemAx = b

Often used in Physical Computational System

Iterative Method

Use conjugate of gradient approach


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CONJUGATE GRADIENT METHOD:

ALGORITHM

Start

k < n

rk+1rT

k+1< etol.

End

0

00

00

k

rpAxbr

kkkk

kkkk

k

T

k

k

T

kk

Aprr

pxx

Apprr

1

1

1

11

111

kk

prp

rr

rr

kkkk

k

T

k

kTk

k

Yes

Yes

No

No


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PARALLEL COMPUTATION:

PERFORMANCE

Parallel computation execution time equation

p

tt

T

commcomp

parallel

tcomp = computation timetcomm = communication timep = number of processors


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PERFORMANCE ASPECTS OF PARALLEL

COMPUTATION

Parallel

Computation

Speedup

GranularityScalability

(Efficiency)

paralel

serial

T

TpS )(

Performance : Problems


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Scheduler

orJob

Manager

Client

Client

Job

All Results

Job

All Results

Task

Results

Task

Results

Worker

Worker

Worker

Task

Results

DISTRIBUTED COMPUTATION SYSTEM

INFRASTRUCTURE DESIGN

ServerSwitch

Server

Worker

Switch

Client

Client


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SYSTEM SPECIFICATION

Node Function Specification QuantityMulticore

Computer Test the performance of parallel programson multicore computersProcessor: Xeon Quadcore (4 core) 2,9

GHz

Memory: 8 GB

NIC: 2 Gigabit Ethernet 1000 MBps

Hardrive: 1x160 GB1

Master

Server

MATLAB parallel computing SchedulerStore the operating system image for Fat

Client configuration Worker

Providing networking applications to

support parallel computing operations

MATLAB

Processor: Dual Core 1,6 GHz

Memory: 2 GB

NIC: Fast Ethernet 100 MBps

Hardrive: 1x80 GB, 1x160 GB1

Worker Run MDCS applications to form MDCScluster systemUndertake the task of Job Manager

Processor: Core 2 Duo 2,6 GHzMemory: 2 GB

NIC: Fast Ethernet 100 MBps 5

Client Running Client SessionRun PCT for parallel computing

Processor: Centrino Core Duo 2x1,6 GHz

Memory: 2,5 GB

NIC: Fast Ethernet 100 MBps 1

Switch Connecting Client, Job Manager, and

Worker physically Fast Ethernet Switch 24 port 1


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RESULT:

SPEEDUP ANALYSIS

Multinode

Multicore


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RESULT:

GRANULARITY ANALYSIS

Multicore

Multinode


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RESULT:

EFFICIENCY ANALYSIS

Multicore

Multinode


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CONCLUSIONS

the performance of parallel execution on multicore computer environment is

better than the performance of parallel execution in multinode environment.

In this research shows that parallel execution on multicore performance is

determined by computational factor than the communication factor. While in

the multinode computing environment, parallel execution is stronglyinfluenced by the communication factor.


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SUGGESTIONS

Further research is needed to study the performance of conjugate gradient

parallel execution with other parallelization strategies, such as using other

parallel programming interface, namely OpenMP, MPICH, etc. In addition, the

parallelization strategy can also be done using other computational units

such as the GPU (Graphics Processing Unit).

To improve the execution performance of parallel conjugate gradient on

multicore, required an increase in the use of hardware, such as using a

computer that has more processor cores, higher clock, and large memory

capacity. As for improving the performance of multinode execution, required

an increase in the use of network hardware that is used such as the use of

Gigabit network interface Ethernet/10 Gigabit Ethernet / Myrinet or use the

IPv6 protocol.


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ACKNOWLEDGEMENT

The author wishes to thank Mr. Rizal Kurniadi as a lecturer also for his advices

for this final assignment.


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REFERENCES

Barney, B. (2011, August 17). Introduction to Parallel Computing. Retrieved August 17,

2011, from Lawrence Livermore National Laboratory:

https://computing.llnl.gov/tutorials/parallel_comp

Grama, A., Gupta, A., Karypis, G., & Kumar, V. (2003). Introduction to Parallel

Computing, 2nd Edition. Edinburgh: Addison Wesley.

Mursito, E. (2009). Pengembangan Komputer Kinerja Tinggi di Teknik Fisika ITB.

Seminar Nasional Teknik Fisika, (pp. 1-8). Bandung.

Schewchuk, J. R. (1994). An Introduction to The Conjugate Gradient Method Without

The Agonizing Pain, Edition 1 1/4. Pittsburgh: School of Computer Science

Carnegie Mellon University.

Schmidt, E. (1908). Title Unknown. Rendiconti del Circolo Matematico di Palermo, (pp.

53-77). Palermo.


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slide scicom - r. m. ibrahim - 10208043

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