Maciej Malawski1, Michał Placek3, Marian Bubak1,2

1 Institute of Computer Science AGH, Mickiewicza 30, 30-059 Kraków, Poland2 Academic Computer Centre CYFRONET, Nawojki 11, 30-950 Kraków, Poland

3 Faculty of Physics and Applied Computer Science AGH Al. Mickiewicza 30, 30-059 Krakow, Poland

{bubak,malawski}@agh.edu.pl, placek@fatcat.ftj.agh.edu.pl

• Clusters of atoms – Very interesting forms between isolated atoms or

molecules and solid state– Important for the technology of constructing

nanoscale devices. • Modeling of clusters

– Several energy minimization methods such as MDSA or L-BFGS,

– Choosing an empirical potential– Highly compute-intensive– The optimal result depends on the number of possible

iterations and initial configurations for each simulation run.

• MOCCA– Common Component Architecture compliant

distributed framework– Based on H2O resource sharing platform

• Features:– Facilitated deployment - easy mechanisms for creation

of components on distributed shared resources - using H2O;

– Efficient communication - both for distributed and local components – using RMIX;

– Flexible - allow flexible configuration of components and various application scenarios;

– Support native components, i.e. components written in non-Java programming languages and compiled for specific architecture – on-going work

• Advantages of component-based approach– Flexibility of composition: from local to distributed

configurations– Additional minimization methods pluggable as

components– Multiple inputs and outputs possible: text file or GUI

(future work)• Experiences with distributed environment

– Multiple annealing components running over many machines

– Support for multiple ports and connections in MOCCA• Future improvements

– From static do dynamic deployment configuration– Tests in Peer-to-Peer environment– Application performance tuning– Native components

Builder

CCACCA

Pluglet Pluglet

Builder Builder

CCACCA

Pluglet Pluglet

BuilderBuilder

CCACCA

Pluglet Pluglet

Builder

MoccaMainBuilder

MoccaMainBuilder

Configuration Generator

Simulated Annealing

Local Minimization

for (i=0; i<100; i++) { generate() simulate();}

Decompose

References1. European Research Network on Foundations, Software Infrastructures and Applications

for Large Scale Distributed, GRID and Peer-to-Peer Technologies. http://www.coregrid.net/

2. M. Malawski, D. Kurzyniec, V. Sunderam, MOCCA - Towards a Distributed CCA Framework for Metacomputing, Proceedings of 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Joint Workshop - HIPS-HPGC, April 4-8, 2005, Denver, Colorado, USA, IEEE Computer Society Press, 2005, pp. 174a.

3. N.T. Wilson and R.L. Johnston: Modeling Gold clusters with an Empirical Many-body Potential, Eur. Phys. J. D 12, 161-169 (2000)

4. CCA forum. The Common Component Architecture (CCA) Forum home page, 2005, http://www.cca-forum.org/.

This research is partly funded by the European Commission Project „CoreGRID”

Component application distributed on multiple H2O kernelsFrom sequential code to distributed components

Example application deployment scenario

Example results

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100125150175200225250275300325350375

MOCCA dis-tributed version

C sequential version

Number of molecules

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puti

ng t

ime[

s]

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25

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75

100

125

150

175

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225

250

MOCCA dis-tributed version

C sequential version

Number of molecules

Com

puti

ng t

ime

per

mol

ecul

e [s

]

Generator Control

Starter

Simulated Annealing

GatherMolecule

Molecule

...

Molecule

Annealing Control

User Input

Outputgenerator

Molecule

Component

H2O Kernel

Legend

Configuration Generator

Simulated Annealing

Storeroom

Local Minimization

Simulated Annealing

Control

Control

Performance tests on a PC cluster–Athlon MP

1800MHz–8 CPUs–Fast Ethernet–SUN Java

J2SE 1.4.2

http://www.icsr.agh.edu.pl/mambo/mocca