Patterns for High Performance Multiscale Computing

Abstract We describe our Multiscale Computing Patterns software for High Performance Multiscale Computing. Following a short review of Multiscale Computing Patterns, this paper introduces the Multiscale Computing Patterns Software, which consists of description, optimisation and execution components. First, the description component translates the task graph, representing a multiscale simulation, to a particular type of multiscale computing pattern. Second, the optimisation component selects and applies algorithms to find the most suitable mapping between submodels and available HPC resources. Third, the execution component which a middleware layer maps submodels to the number and type of physical resources based on the suggestions emanating from the optimisation part together with infrastructure-specific metrics such as queueing time and resource availability. The main purpose of the Multiscale Computing Patterns software is to leverage the Multiscale Computing Patterns to simplify and automate the execution of complex multiscale simulations on high performance computers, and to provide both application-specific and pattern-specific performance optimisation. We test the performance and the resource usage for three multiscale models, which are expressed in terms of two Multiscale Computing Patterns. In doing so, we demonstrate how the software automates resource selection and load balancing, and delivers performance benefits from both the end-user and the HPC system level perspectives.

[1]  O. Sauter,et al.  Corrigendum: The European Integrated Tokamak Modelling (ITM) effort: achievements and first physics results (2014 Nucl. Fusion 54 043018) , 2014 .

[2]  Alfons G. Hoekstra,et al.  Toward a Complex Automata Formalism for Multi-Scale Modeling , 2007 .

[3]  Pavel S. Zun,et al.  Towards the virtual artery: a multiscale model for vascular physiology at the physics–chemistry–biology interface , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[4]  Peter V. Coveney,et al.  Flexible composition and execution of high performance, high fidelity multiscale biomedical simulations , 2012, Interface Focus.

[5]  Peter V. Coveney,et al.  Survey of Multiscale and Multiphysics Applications and Communities , 2012, Computing in Science & Engineering.

[6]  Krzysztof Kurowski,et al.  Development of Science Gateways Using QCG — Lessons Learned from the Deployment on Large Scale Distributed and HPC Infrastructures , 2016, Journal of Grid Computing.

[7]  Jan Weglarz,et al.  DCworms - A tool for simulation of energy efficiency in distributed computing infrastructures , 2013, Simul. Model. Pract. Theory.

[8]  Peter V. Coveney,et al.  Multiscale computing in the exascale era , 2016, J. Comput. Sci..

[9]  Derek Gaston,et al.  MOOSE: A parallel computational framework for coupled systems of nonlinear equations , 2009 .

[10]  Christopher January,et al.  Allinea MAP: Adding Energy and OpenMP Profiling Without Increasing Overhead , 2015 .

[11]  Krzysztof Kurowski,et al.  New QosCosGrid Middleware Capabilities and Its Integration with European e-Infrastructure , 2014, PL-Grid.

[12]  Peter M. A. Sloot,et al.  Multi-scale modelling in computational biomedicine , 2010, Briefings Bioinform..

[13]  David J. Hill,et al.  Nuclear energy for the future. , 2008, Nature materials.

[14]  Krzysztof Kurowski,et al.  New Capabilities in QosCosGrid Middleware for Advanced Job Management, Advance Reservation and Co-allocation of Computing Resources - Quantum Chemistry Application Use Case , 2012, PL-Grid.

[15]  Alfons G. Hoekstra,et al.  Foundations of distributed multiscale computing: Formalization, specification, and analysis , 2013, J. Parallel Distributed Comput..

[16]  Jarek Nabrzyski,et al.  A multicriteria approach to two-level hierarchy scheduling in grids , 2008, J. Sched..

[17]  Peter V Coveney,et al.  Rapid, Accurate, Precise, and Reliable Relative Free Energy Prediction Using Ensemble Based Thermodynamic Integration. , 2017, Journal of chemical theory and computation.

[18]  P. Coveney,et al.  Continuum-particle hybrid coupling for mass, momentum, and energy transfers in unsteady fluid flow. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  F. Inti Pelupessy,et al.  Multi-physics simulations using a hierarchical interchangeable software interface , 2011, Comput. Phys. Commun..

[20]  Gábor Závodszky,et al.  Hemocell: a high-performance microscopic cellular library , 2017, ICCS.

[21]  Jarek Nabrzyski,et al.  Multicriteria aspects of Grid resource management , 2004 .

[22]  Ana María Cetto,et al.  The Nuclear energy of the future , 2009 .

[23]  Bastien Chopard,et al.  Multiscale modelling and simulation: a position paper , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  Gábor Závodszky,et al.  Cellular Level In-silico Modeling of Blood Rheology with An Improved Material Model for Red Blood Cells , 2017, Front. Physiol..

[25]  Peter V. Coveney,et al.  Distributed multiscale computing with MUSCLE 2, the Multiscale Coupling Library and Environment , 2013, J. Comput. Sci..

[26]  J. Iqbal,et al.  Endothelial repair process and its relevance to longitudinal neointimal tissue patterns: comparing histology with in silico modelling , 2014, Journal of The Royal Society Interface.

[27]  P. V. Coveney,et al.  Performance of distributed multiscale simulations , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[28]  O. Borodin,et al.  Advancing a distributed multi-scale computing framework for large-scale high-throughput discovery in materials science , 2015, Nanotechnology.

[29]  Peter M. A. Sloot,et al.  Introducing Grid Speedup G: A Scalability Metric for Parallel Applications on the Grid , 2005, EGC.

[30]  Derek Groen,et al.  Chemically Specific Multiscale Modeling of Clay–Polymer Nanocomposites Reveals Intercalation Dynamics, Tactoid Self-Assembly and Emergent Materials Properties , 2014, Advanced materials.

[31]  S. Valcke,et al.  The OASIS3 coupler: a European climate modelling community software , 2012 .

[32]  Orestis Malaspinas,et al.  Parallel performance of an IB-LBM suspension simulation framework , 2015, J. Comput. Sci..

[33]  Joris Borgdorff,et al.  A framework for multi-scale modelling , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[34]  Alfons G. Hoekstra,et al.  Towards a Complex Automata Framework for Multi-scale Modeling: Formalism and the Scale Separation Map , 2007, International Conference on Computational Science.

[35]  Peter V. Coveney,et al.  FabSim: Facilitating computational research through automation on large-scale and distributed e-infrastructures , 2015, Comput. Phys. Commun..

[36]  Joris Borgdorff,et al.  Designing and running turbulence transport simulations using a distributed multiscale computing approach , 2013 .

[38]  Alfons G. Hoekstra,et al.  Complex Automata: Multi-scale Modeling with Coupled Cellular Automata , 2010, Simulating Complex Systems by Cellular Automata.