1. 1 The Mapper project receives funding from the EC's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° RI-261507. Towards Environment for Multiscale Applications Katarzyna Rycerz, Eryk Ciepiela, Joanna Kocot, Marcin Nowak, Pawel Pierzchała, Marian Bubak KUKDM, Zakopane, 9-11.03 2011

2. 2 Overview Multiscale simulations - overview Tools for multiscale simulations MUSCLE-based multiscale application in GridSpace Example demo with In-stent restenosis application Summary and future work

3. 3 Multiscale Simulations Consists of modules of different scale Examples – e.g. modelling: virtual physiological human initiative reacting gas flows capillary growth colloidal dynamics stellar systems and many more... the reoccurrence of stenosis, a narrowing of a blood vessel, leading to restricted blood flow

4. 4 Tools for multiscale simulations Model Couling Toolkit Applies a message passing (MPI) style of communication between simulation models. Oriented towards domain data decomposition of the simulated problem Provides a support for advanced data transformations between different models J. Larson, R. Jacob, E. Ong ”The Model Coupling Toolkit: A New Fortran90 Toolkit for Building Multiphysics Parallel Coupled Models.” 2005: Int. J. High Perf. Comp. App.,19(3), 277-292. Astrophysical Multi-Scale Environment (AMUSE) Scripting approach (Python) is used to couple models together., MPI used to distribute modules Astrophysical models : stellar evolution, hydrodynamics, stellar dynamics and radiative transfer S. Portegies Zwart, S. McMillan, at al. A Multiphysics and Multiscale Software Environment for Modeling Astrophysical Systems, New Astronomy, volume 14, issue 4, year 2009, pp. 369 - 378 High Level Architecture components : Application components run concurrently and communicate using HLA mechanisms Coponents are steerable from outside during runtime using script interface Support for synchronisation between multiscale modules - time stamps, advanced time management K. Rycerz, M. Bubak, P. M. A. Sloot: HLA Component Based Environment For Distributed Multiscale Simulations In: T. Priol and M. Vanneschi (Eds.), From Grids to Service and Pervasive Computing, Springer, 2008, pp. 229-239

5. 5 Goals Support composition of simulation models. scripting approach to programmatically access simulation components to build multi-disciplinary and multi-scale “in silico” experiments Support execution of such experiments and achieve their reusability Integrate solutions designed for multiscale simulations’ development with possibilities given by: tools for multiscale simulations environments for application composition computational e-Infrastructures

6. 6 Experiment workbench Constructing experiment plans from code snippets Interactively run experiments Experiment Execution Environment Multiple interpreters Access to libraries, programs and services (gems) Access to computing infrastructure: Cluster, grid, cloud Experience Virolab project PL-Grid NGI E. Ciepiela, D. Harezlak, J. Kocot, T. Bartynski, M. Kasztelnik, P. Nowakowski, T. Gubała, M. Malawski, M. Bubak; Exploratory Programming in the Virtual Laboratory, in Proceedings of the International Multiconference on Computer Science and Information Technology pp. 621– 628. GridSpace

7. 7 Multiscale Coupling Library and Environment (MUSCLE) Provides a software framework to build simulations according to the complex automata theory Introduces concept of kernels that communicate by unidirectional pipelines dedicated to pass a specific kind of data from/to a kernel (asynchronous communication) J. Hegewald, M. Krafczyk, J. Tlke, A. G. Hoekstra, and B. Chopard. An agent-based coupling platform for complex automata. ICCS, volume 5102 of Lecture Notes in Computer Science, pages 227233. Springer, 2008. # CxA configuration of sample application # configure cxa properties cxa = Cxa.LAST cxa.env["max_timesteps"] = 2 cxa.env["cxa_path"] = File.dirname(__FILE__) # declare kernels cxa.add_kernel('w', 'examples.simplejava.Sender') cxa.add_kernel('r', 'examples.simplejava.ConsoleWriter') # configure connection scheme cs = cxa.cs cs.attach('w' => 'r') { tie('data', 'data') } Sender Module Console Module Sample application MUSCLE communication

8. 8 GridSpace for MUSCLE application Integrated environment for: Configuring modules connections and parameters in cxa file Visualizing modules connections Running application on a chosen e-infrastructure Interactive post-processing the output using various tools (e.g. MATLAB)

9. 9 MUSCLE application in GridSpace GridSpace Experiment host: Interpreters and libraries accessing PBS User files GridSpace Experiment Workbench QCG Grid Resource Management System BF Module SMC Module DD Module other Modules QCG infrastructure MUSCLE communication BF Module SMC Module DD Module other Modules Local DRMS (PBS) MUSCLE communication MUSCLE CxA Graphical Viewer - Models connections Various scripts editors General (Python, Ruby, Perl..) Specific (Matlab, Mathematica, CxA interpreter) Infrastructure access layer User file management -simulation output view cluster

10. 10 Tool for automatic MUSCLE application distribution Main features Accessible from GridSpace level Automatically distributes MUSCLE applications in GRID environment Live stdout/stderr streaming Based on Distributed Ruby (DRb) and PBS

11. 11 Demo – Instent restenosis in GridSpace http://www.youtube.com/watch?v=3S9- kljyXIwhttp://www.youtube.com/watch?v=3S9- kljyXIw

12. 12 Summary and Future Work GridSpace can be used as a high level tool for setting up and running MUSCLE-based multiscale applications We plan to extend our solution to a set of tools supporting programming and execution of multiscale applications in general To control and test behaviour of such applications we plan to support creation of their skeletons parametrised „empty” multiscale application of the same structure and requirements as the real one. We plan support for various European e-infrastructures and cloud resources See: http://dice.cyfronet.pl

13. 13 MAPPER architecture Develop computational strategies, software and services for distributed multiscale simulations across disciplines exploiting existing and evolving European e-infrastructure Deploy a computational science infrastructure Deliver high quality components aiming at large-scale, heterogeneous, high performance multi-disciplinary multiscale computing. Advance state-of-the-art in high performance computing on e- infrastructures enable distributed execution of multiscale models across e- Infrastructures,