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Desktop Grids for International Scientific Collaboration

International Desktop Grid Federation

APPLICATION OF DESKTOP GRID TECHNOLOGY IN MATERIAL SCIENCE

A.Gatsenko, A.Baskova, Yu.GordienkoG.V.Kurdyumov Institute for Metal Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine

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Simulation of structure and mechanical properties of materials is extremely important in materials science to quantify deformation and strength characteristics of materials. Among variety of new materials the special place is occupied by materials that have nanoscale structure (nanomaterials), such as metal nanocrystals and nanoscale non-metallic materials with unique properties (nanotubes, graphene).

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Problem

Molecular dynamics (MD) simulations for realistic configurations take:

• huge resources of supercomputers• large shared memory• big number of CPUs.

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Way to Solution

The distributed computing (DC) model on the basis of:-BOINC,-XtremWeb-HEP, -OurGrid, -EDGeS-bridge,-WS-PGRADE

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Main Aim

To demonstrate the capabilities of the proposed technical solutions to the example of modeling of physical processes:

• tension of nanocrystals in different conditions• tension ensemble of nanocrystals• simulation graphene

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Technical solution : Open-Source Simulator - LAMMPS

LAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator – by Sandia National Laboratories with:• scripts for pre- and post-processing,• multi-core CPU and GPU support,• checkpointing support;• intrinsic message passing,• NO explicit DCI support!

Very popular: numerous users/publications -->

LAMMPS

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Porting to DCI on Desktop Grid

LAMMPS

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Operational production infrastructure

IMP team maintained and scaled-up DG BOINC infrastructure at the premises of SLinCA@Home IMP Desktop Grid (http://dg.imp.kiev.ua/slinca). From December 10, 2010 it works on the permanent basis with public access of workers (users).

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Operational production infrastructure

The current status of IMP Desktop Grid infrastructure is “under tests of scaling-up” and “under tests of new applications”.SLinCA@Home DG (http://dg.imp.kiev.ua/slinca) was scaled-up from 1500 to > 3000 workers (users); from 10000 to 20 000 in-progress workunits. The current average performance is ~150 GFLOPs with weekly peaks of 550 GFLOPs

Last 4-weak performance Number of in-progress workunits

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Technical Solution - Conclusions

Using the Desktop Grid technology with the assistance of volunteer computing resources quickly and easily achieve the required level of performance.

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Demonstrations for different physical processes:

• single nanocrystals under different tension conditions,

• ensemble of nanocrystals under tension,

• simulation of graphene under different tension conditions

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Physical process 1:Tension of nanocrystals in

different parameters (conditions)

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Simulation of nano-crystal Al:Typical Sweeping Parameters…

External mechanical influence

with different values of increasing strain…

strain

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Simulation of nano-crystal Al: Typical Sweeping Parameters…

External mechanical influence

with differentcrystal orientations…

strain

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Video of Al nano crystal strain

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Simulation of nano-crystal Al: Typical Sweeping Parameters…

External mechanical influence

with different values ofrate…

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Practical Results

Physical parameter decomposition for “parameter sweeping” parallelism allow us to widen a range of

simulated parameters and find their “magic” (critical) values for atomic self-organization…

3D hierarchic network of voids in Al bulk

2D super-lattice on Al surface

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Physical Process 1 - Conclusions

• the estimation of the influence of various parameters on the process of deformation of materials.

• the regimes allow you to create a given structure.

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Physical process 2:Tension ensemble of nanocrystals with different statistical realizations

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……

..

Fitting PDF and CDF to Weibull distribution 

Distribution (PDF) of concentrations of defects

in the ensemble of ~1000 samples 

Drift of PDF (from normal to Weibull) in ensemble of ~1000 samples: 

quantity->qualitative change

Simulation of nano-crystal Al: Statistical analysis

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Physical Process 2 - Conclusions

Change of defect distribution (from normal to Weibull) is followed by qualitative change of plastic deformation mode (from homogeneous strain to localized mode and… fracture!).

Physical process 3:Simulation of graphene for

different parameters

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Simulation of graphene

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Simulation of graphene platesTypical Sweeping Parameters…

The size effect for different sizes of plates…

from 2x2 nm to 2x32 nm

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Simulation of graphene platesTypical Sweeping Parameters…

“Tersoff” “Airebo”

influence of the type of interatomic potential…

strain strain

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0,0 0,1 0,2 0,3 0,4

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0

100

200

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Stre

ss.,

GP

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Strain

2x2 nm(1) 2x4 nm(2) 2x8 nm(3) 2x16 nm(4)

stress dependence of the tensile strain

fracture

Practical Results

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Physical Process 3 - Conclusions

• Qualitative and quantitative analysis of the process of deformation and fracture of graphene occurs fragile scenario without the formation of stable defect substructure.

• A comparative analysis of the effect of different potentials (Airebo / Tersoff) on qualitative and quantitative process of deformation of graphene.

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General Conclusions

• It is shown that the mechanical characteristics evaluated on the basis of MD simulations using LAMMPS package in the DG-SG DCI are in satisfactory agreement with the experimental data and allowed to discover the new aspects of deformation and fracture mechanisms in nanomaterials

• porting MD-applications to DG-SG DCI is easy, if: BOINC SZTAKI DC-API and SG-DG EDGeS Bridge are used; parameter decomposition and sweeping

parallelism is possible; message passing is localized at worker side (in multi core CPU/GPU).

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Acknowledgements

This work is partially funded by FP7 DEGISCO (Desktop Grids for International Scientific Collaboration) (http://degisco.eu).

DEGISCO project is supported by the FP7 Capacities Programme under grant agreement number RI-261561

Thank you for your attention!