Tools Nanomat.

Bloodflow

Multiscale Communities

Tools

Wide area message passing

Connect applications running on different platforms by establishing communication paths.Each path can be hand-tuned for better performance.More light-weight than MPI, useful for coupling and parallelizing codes over long distances.

AppsCosmological N-body simulation across supercomputers. Here MPWide facilitates wide area message passing over the wide area networks.

Apps 1D 3D

supercomputer

Multiscale bloodflow modelling. Here we use MPWide to efficiently exchange data between a desktop in London and a supercomputer in Edinburgh.

● Couples computational models.● Connects these over wide area networks

using MPWide.● Handles models’ time and space scales as

per the Multiscale Modeling and Simulation framework*.

● Supports Java, C, C++ and Fortran.● Used by 10+ production applications.

More?

http://www.github.com/djgroen/MPWidehttp://www.qoscosgrid.org/trac/muscle

Bloodflow

● Aim: Accurately model cerebrovascular bloodflow with acceptable performance.

● Approach: integrate a person-specific circulation model with a high-res local vasculature model.

● Future applications:○ Comparison of rheology models.○ Validation against medical data (ongoing).○ Look for predictive indicators of aneurysm rupture.

● And eventually predict the outcome of cerebrovascular surgery.

Example visualization

HECToR @ EPCC

SuperMUC @ LRZ

1D 3D

supercomputer

We couple the 1D Python Navier-Stokes (PyNS) solver to HemeLB to construct a multiscale model..

We use MPWide to efficiently exchange data between a desktop in London and a supercomputer in Edinburgh.

More?

Groen et al., Interface Focus 3(2), 2013.Groen et al., Journal of Computational Science 4(5), 2013.Bernabeu et al., Interface Focus 3(2), 2013.http://www.slideshare.net/DerekGroen/multiscale-modelling-of-brain-bloodflow

Nanomaterials

Aim: To develop quantitative coarse-grained models of clay-polymer nanocomposites.We will use these models to:● Predict the thermodynamically favourable state of the

composites.● Predict their elasticity.

We require:● Accurate potentials.● Realistic structures.● Task farming many MD simulations.

Atomistic representation of a charged clay sheet

Coarse-grained representation of a charged clay sheet

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4

More?Suter, Groen, Kabalan and Coveney, MRS Proceedings 1470, 2012.Borgdorff et al., "Multiscale Simulations on distributed European e-Infrastructures", inSiDE, Vol. 10, No. 1, Spring 2012.Suter, Coveney, Anderson, Greenwell and Cliffe, Energy Environ. Sci., 2011.More papers soon. :)

Communities

Groen, Zasada, Coveney, accepted by CiSE, 2013.

Groen, Zasada, Coveney, accepted by CiSE, 2013.

Scientific Challenges

Just scratching the surface here:● Which couplings can deliver useful

information?● What information should we exchange?● How do we validate and error-check coupled

models?○ ...what if they are multi-physics as well?

Computational Challenges

● Where to simulate the models?● How do we couple?● Can we make it fast?● Can we make it reusable?● How do we analyze the resulting data?

More?

“Survey of Multiscale and Multiphysics Applications and Communities”Derek Groen, Stefan Zasada and Peter CoveneyIEEE Computing in Science & Engineering (in press), 2013.preprint at: http://arxiv.org/1208.6444

Acknowledgements

Slides made by Derek GroenThanks go out to:James Suter, Rupert Nash, James Hetherington, Peter Coveney, Hywel Carver, Stefan Zasada, Steven Rieder, Simon Portegies Zwart, Chris Kurowski, Alfons Hoekstra, Werner Dubitzky...and many others!