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Kusch L, Diaz-Pier S, Klijn W, Sontheimer K, Bernard C, Morrison A, Jirsa V. Multiscale co-simulation design pattern for neuroscience applications. Front Neuroinform 2024; 18:1156683. [PMID: 38410682 PMCID: PMC10895016 DOI: 10.3389/fninf.2024.1156683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 01/19/2024] [Indexed: 02/28/2024] Open
Abstract
Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.
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Affiliation(s)
- Lionel Kusch
- Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wouter Klijn
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Kim Sontheimer
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Christophe Bernard
- Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France
| | - Abigail Morrison
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Forschungszentrum Jülich GmbH, IAS-6/INM-6, JARA, Jülich, Germany
- Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France
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2
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Zhang W, Valencia A, Chang NB. Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:2170-2190. [PMID: 34473633 DOI: 10.1109/tnnls.2021.3106777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents' behavioral patterns. With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents' behavioral rules into an adaptive model. Even though there are plenty of studies that apply ML in ABMs, the generalized applicable scenarios, frameworks, and procedures for implementations are not well addressed. In this article, we provide a comprehensive review of applying ML in ABM based on four major scenarios, i.e., microagent-level situational awareness learning, microagent-level behavior intervention, macro-ABM-level emulator, and sequential decision-making. For these four scenarios, the related algorithms, frameworks, procedures of implementations, and multidisciplinary applications are thoroughly investigated. We also discuss how ML can improve prediction in ABMs by trading off the variance and bias and how ML can improve the sequential decision-making of microagent and macrolevel policymakers via a mechanism of reinforced behavioral intervention. At the end of this article, future perspectives of applying ML in ABMs are discussed with respect to data acquisition and quality issues, the possible solution of solving the convergence problem of reinforcement learning, interpretable ML applications, and bounded rationality of ABM.
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3
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Multiscale modeling and integration of a combined cycle power plant and a two-tank thermal energy storage system with gPROMS and SimCentral. KOREAN J CHEM ENG 2021. [DOI: 10.1007/s11814-021-0789-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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4
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Luk OO, Lakhlili J, Hoenen O, von Toussaint U, Scott BD, Coster DP. Towards validated multiscale simulations for fusion. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200074. [PMID: 33775143 DOI: 10.1098/rsta.2020.0074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Harnessing energy produced by thermonuclear fusion reactions has the potential to provide a clean and inexpensive source of energy to Earth. However, throughout the past seven decades, physicists learned that creating our very own fusion energy source is very difficult to achieve. We constructed a component-based, multiscale fusion workflow to model fusion plasma inside the core of a tokamak device. To ensure the simulation results agree with experimental values, the model needs to undergo the process of verification, validation and uncertainty quantification (VVUQ). This paper will go over the VVUQ work carried out in the multiscale fusion workflow (MFW), with the help of the EasyVVUQ software library developed by the VECMA project. In particular, similarity of distributions from simulation and experiment is explored as a validation metric. Such initial validation measures provide insights into the accuracy of the simulation results. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.
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Affiliation(s)
- O O Luk
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
| | - J Lakhlili
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
| | - O Hoenen
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
| | | | - B D Scott
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
| | - D P Coster
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
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5
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Groen D, Arabnejad H, Jancauskas V, Edeling WN, Jansson F, Richardson RA, Lakhlili J, Veen L, Bosak B, Kopta P, Wright DW, Monnier N, Karlshoefer P, Suleimenova D, Sinclair R, Vassaux M, Nikishova A, Bieniek M, Luk OO, Kulczewski M, Raffin E, Crommelin D, Hoenen O, Coster DP, Piontek T, Coveney PV. VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200221. [PMID: 33775151 PMCID: PMC8059654 DOI: 10.1098/rsta.2020.0221] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/10/2020] [Indexed: 05/04/2023]
Abstract
We present the VECMA toolkit (VECMAtk), a flexible software environment for single and multiscale simulations that introduces directly applicable and reusable procedures for verification, validation (V&V), sensitivity analysis (SA) and uncertainty quantication (UQ). It enables users to verify key aspects of their applications, systematically compare and validate the simulation outputs against observational or benchmark data, and run simulations conveniently on any platform from the desktop to current multi-petascale computers. In this sequel to our paper on VECMAtk which we presented last year [1] we focus on a range of functional and performance improvements that we have introduced, cover newly introduced components, and applications examples from seven different domains such as conflict modelling and environmental sciences. We also present several implemented patterns for UQ/SA and V&V, and guide the reader through one example concerning COVID-19 modelling in detail. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.
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Affiliation(s)
- D. Groen
- Department of Computer Science, Brunel University London, London, UK
- Centre for Computational Science, University College London, London, UK
| | - H. Arabnejad
- Department of Computer Science, Brunel University London, London, UK
| | | | - W. N. Edeling
- Centrum Wiskunde and Informatica, Amsterdam, The Netherlands
| | - F. Jansson
- Centrum Wiskunde and Informatica, Amsterdam, The Netherlands
- Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
| | - R. A. Richardson
- Centre for Computational Science, University College London, London, UK
- Netherlands eScience Center, Amsterdam, The Netherlands
| | - J. Lakhlili
- Max Planck Institute for Plasma Physics - Garching, Munich, Germany
| | - L. Veen
- Netherlands eScience Center, Amsterdam, The Netherlands
| | - B. Bosak
- Poznań Supercomputing and Networking Center, Poznań, Poland
| | - P. Kopta
- Poznań Supercomputing and Networking Center, Poznań, Poland
| | - D. W. Wright
- Centre for Computational Science, University College London, London, UK
| | - N. Monnier
- CEPP - Center for Excellence in Performance Programming, Atos Bull, Rennes, France
| | - P. Karlshoefer
- CEPP - Center for Excellence in Performance Programming, Atos Bull, Rennes, France
| | - D. Suleimenova
- Department of Computer Science, Brunel University London, London, UK
| | - R. Sinclair
- Centre for Computational Science, University College London, London, UK
| | - M. Vassaux
- Centre for Computational Science, University College London, London, UK
| | - A. Nikishova
- Computational Science Lab, Institute for Informatics, University of Amsterdam, Amsterdam, The Netherlands
| | - M. Bieniek
- Centre for Computational Science, University College London, London, UK
| | - Onnie O. Luk
- Max Planck Institute for Plasma Physics - Garching, Munich, Germany
| | - M. Kulczewski
- Poznań Supercomputing and Networking Center, Poznań, Poland
| | - E. Raffin
- CEPP - Center for Excellence in Performance Programming, Atos Bull, Rennes, France
| | - D. Crommelin
- Centrum Wiskunde and Informatica, Amsterdam, The Netherlands
- Korteweg-de Vries Institute for Mathematics, Amsterdam, The Netherlands
| | - O. Hoenen
- Max Planck Institute for Plasma Physics - Garching, Munich, Germany
| | - D. P. Coster
- Max Planck Institute for Plasma Physics - Garching, Munich, Germany
| | - T. Piontek
- Poznań Supercomputing and Networking Center, Poznań, Poland
| | - P. V. Coveney
- Centre for Computational Science, University College London, London, UK
- Computational Science Lab, Institute for Informatics, University of Amsterdam, Amsterdam, The Netherlands
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7
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Kotsalos C, Latt J, Beny J, Chopard B. Digital blood in massively parallel CPU/GPU systems for the study of platelet transport. Interface Focus 2021; 11:20190116. [PMID: 33335703 PMCID: PMC7739916 DOI: 10.1098/rsfs.2019.0116] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/23/2020] [Indexed: 01/13/2023] Open
Abstract
We propose a highly versatile computational framework for the simulation of cellular blood flow focusing on extreme performance without compromising accuracy or complexity. The tool couples the lattice Boltzmann solver Palabos for the simulation of blood plasma, a novel finite-element method (FEM) solver for the resolution of deformable blood cells, and an immersed boundary method for the coupling of the two phases. The design of the tool supports hybrid CPU-GPU executions (fluid, fluid-solid interaction on CPUs, deformable bodies on GPUs), and is non-intrusive, as each of the three components can be replaced in a modular way. The FEM-based kernel for solid dynamics outperforms other FEM solvers and its performance is comparable to state-of-the-art mass-spring systems. We perform an exhaustive performance analysis on Piz Daint at the Swiss National Supercomputing Centre and provide case studies focused on platelet transport, implicitly validating the accuracy of our tool. The tests show that this versatile framework combines unprecedented accuracy with massive performance, rendering it suitable for upcoming exascale architectures.
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Affiliation(s)
- Christos Kotsalos
- Computer Science Department, University of Geneva, 7 route de Drize, 1227 Carouge, Switzerland
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8
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Zanin M, Chorbev I, Stres B, Stalidzans E, Vera J, Tieri P, Castiglione F, Groen D, Zheng H, Baumbach J, Schmid JA, Basilio J, Klimek P, Debeljak N, Rozman D, Schmidt HHHW. Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine. Brief Bioinform 2020; 20:1057-1062. [PMID: 29220509 PMCID: PMC6135236 DOI: 10.1093/bib/bbx160] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 10/11/2017] [Indexed: 12/13/2022] Open
Abstract
Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.
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Affiliation(s)
| | - Ivan Chorbev
- Faculty for Computer Science and Engineering, University Ss. Cyril and Methodius in Skopje
| | - Blaz Stres
- Microbiology at University of Ljubljana, Slovenia
| | | | - Julio Vera
- Systems Tumor Immunology at the FAU Erlangen-Nuremberg, Germany
| | - Paolo Tieri
- Network biology, systems medicine and theoretical immunology
| | | | - Derek Groen
- Simulation and Modelling at Brunel University London
| | - Huiru Zheng
- Computer Science at Ulster University, United Kingdom
| | - Jan Baumbach
- Computational Biomedicine, University of Southern Denmark
| | - Johannes A Schmid
- Inflammation, cardiovascular diseases and cancer, at molecular, cellular and clinical levels
| | | | - Peter Klimek
- Section for Science of Complex Systems at the Medical University of Vienna
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9
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Easing Multiscale Model Design and Coupling with MUSCLE 3. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7304760 DOI: 10.1007/978-3-030-50433-5_33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Multiscale modelling and simulation typically entails coupling multiple simulation codes into a single program. Doing this in an ad-hoc fashion tends to result in a tightly coupled, difficult-to-change computer program. This makes it difficult to experiment with different submodels, or to implement advanced techniques such as surrogate modelling. Furthermore, building the coupling itself is time-consuming. The MUltiScale Coupling Library and Environment version 3 (MUSCLE 3) aims to alleviate these problems. It allows the coupling to be specified in a simple configuration file, which specifies the components of the simulation and how they should be connected together. At runtime a simulation manager takes care of coordination of submodels, while data is exchanged over the network in a peer-to-peer fashion via the MUSCLE library. Submodels need to be linked to this library, but this is minimally invasive and restructuring simulation codes is usually not needed. Once operational, the model may be rewired or augmented by changing the configuration, without further changes to the submodels. MUSCLE 3 is developed openly on GitHub, and is available as Open Source software under the Apache 2.0 license.
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10
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Establishment of a Numerical Model to Design an Electro-Stimulating System for a Porcine Mandibular Critical Size Defect. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9102160] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Electrical stimulation is a promising therapeutic approach for the regeneration of large bone defects. Innovative electrically stimulating implants for critical size defects in the lower jaw are under development and need to be optimized in silico and tested in vivo prior to application. In this context, numerical modelling and simulation are useful tools in the design process. In this study, a numerical model of an electrically stimulated minipig mandible was established to find optimal stimulation parameters that allow for a maximum area of beneficially stimulated tissue. Finite-element simulations were performed to determine the stimulation impact of the proposed implant design and to optimize the electric field distribution resulting from sinusoidal low-frequency ( f = 20 Hz ) electric stimulation. Optimal stimulation parameters of the electrode length h el = 25 m m and the stimulation potential φ stim = 0.5 V were determined. These parameter sets shall be applied in future in vivo validation studies. Furthermore, our results suggest that changing tissue properties during the course of the healing process might make a feedback-controlled stimulation system necessary.
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11
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Hoekstra AG, Portegies Zwart S, Coveney PV. Multiscale modelling, simulation and computing: from the desktop to the exascale. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180355. [PMID: 30967039 PMCID: PMC6388007 DOI: 10.1098/rsta.2018.0355] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/11/2018] [Indexed: 05/02/2023]
Abstract
This short contribution introduces a theme issue dedicated to 'Multiscale modelling, simulation and computing: from the desktop to the exascale'. It holds a collection of articles presenting cutting-edge research in generic multiscale modelling and multiscale computing, and applications thereof on high-performance computing systems. The special issue starts with a position paper to discuss the paradigm of multiscale computing in the face of the emerging exascale, followed by a review and critical assessment of existing multiscale computing environments. This theme issue provides a state-of-the-art account of generic multiscale computing, as well as exciting examples of applications of such concepts in domains ranging from astrophysics, via material science and fusion, to biomedical sciences. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
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Affiliation(s)
- Alfons G. Hoekstra
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
- ITMO University, Saint Petersburg, Russia
| | | | - Peter V. Coveney
- Centre for Computational Science, Department of Chemistry, University College London, London, England
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12
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Nikishova A, Veen L, Zun P, Hoekstra AG. Semi-intrusive multiscale metamodelling uncertainty quantification with application to a model of in-stent restenosis. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180154. [PMID: 30967038 PMCID: PMC6388010 DOI: 10.1098/rsta.2018.0154] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/10/2018] [Indexed: 05/02/2023]
Abstract
We explore the efficiency of a semi-intrusive uncertainty quantification (UQ) method for multiscale models as proposed by us in an earlier publication. We applied the multiscale metamodelling UQ method to a two-dimensional multiscale model for the wound healing response in a coronary artery after stenting (in-stent restenosis). The results obtained by the semi-intrusive method show a good match to those obtained by a black-box quasi-Monte Carlo method. Moreover, we significantly reduce the computational cost of the UQ. We conclude that the semi-intrusive metamodelling method is reliable and efficient, and can be applied to such complex models as the in-stent restenosis ISR2D model. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
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Affiliation(s)
- A. Nikishova
- Computational Science Lab, Institute for Informatics, Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - L. Veen
- Netherlands eScience Center, 1098 XG Amsterdam, The Netherlands
| | - P. Zun
- Computational Science Lab, Institute for Informatics, Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
- ITMO University, Saint Petersburg, 197101, Russia
| | - A. G. Hoekstra
- Computational Science Lab, Institute for Informatics, Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
- ITMO University, Saint Petersburg, 197101, Russia
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13
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Hoekstra AG, Chopard B, Coster D, Portegies Zwart S, Coveney PV. Multiscale computing for science and engineering in the era of exascale performance. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180144. [PMID: 30967040 PMCID: PMC6388008 DOI: 10.1098/rsta.2018.0144] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/09/2018] [Indexed: 05/18/2023]
Abstract
In this position paper, we discuss two relevant topics: (i) generic multiscale computing on emerging exascale high-performing computing environments, and (ii) the scaling of such applications towards the exascale. We will introduce the different phases when developing a multiscale model and simulating it on available computing infrastructure, and argue that we could rely on it both on the conceptual modelling level and also when actually executing the multiscale simulation, and maybe should further develop generic frameworks and software tools to facilitate multiscale computing. Next, we focus on simulating multiscale models on high-end computing resources in the face of emerging exascale performance levels. We will argue that although applications could scale to exascale performance relying on weak scaling and maybe even on strong scaling, there are also clear arguments that such scaling may no longer apply for many applications on these emerging exascale machines and that we need to resort to what we would call multi-scaling. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
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Affiliation(s)
- Alfons G. Hoekstra
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, The Netherlands
- High Performance Computing Department, ITMO University, St Petersburg, Russia
| | - Bastien Chopard
- Department of Computer Science, University of Geneva, Switzerland
| | | | | | - Peter V. Coveney
- The Centre for Computational Science, Department of Chemistry, University College London, UK
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14
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Luk OO, Hoenen O, Perks O, Brabazon K, Piontek T, Kopta P, Bosak B, Bottino A, Scott BD, Coster DP. Application of the extreme scaling computing pattern on multiscale fusion plasma modelling. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180152. [PMID: 30967036 PMCID: PMC6388005 DOI: 10.1098/rsta.2018.0152] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/23/2018] [Indexed: 05/18/2023]
Abstract
The extreme scaling pattern of the ComPat project is applied to a multi-scale workflow relevant to the magnetically confined fusion problem. This workflow combines transport, turbulence and equilibrium codes (together with additional auxiliaries such as initial conditions and numerical module), which aims at calculating the behaviour of a fusion plasma on long (transport) time scales based on information from much faster (turbulence) time scales. Initial findings of profile measurements are reported in this paper and indicate that, depending on the chosen performance metric for defining 'cost', such as time to completion, efficiency and total energy consumption of the mutliscale workflow, different choices on the number of cores would be made when determining the optimal execution configuration. A variant of the workflow which increases the inherent parallelism is presented, and shown to produce equivalent results at (typically) lower cost compared with the original workflow. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
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Affiliation(s)
- O. O. Luk
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
| | - O. Hoenen
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
| | | | | | - T. Piontek
- Poznań Supercomputing and Networking Center, Poznań, Poland
| | - P. Kopta
- Poznań Supercomputing and Networking Center, Poznań, Poland
| | - B. Bosak
- Poznań Supercomputing and Networking Center, Poznań, Poland
| | - A. Bottino
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
| | - B. D. Scott
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
| | - D. P. Coster
- Max-Planck-Institut für Plasmaphysik, Garching, Germany
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15
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Liberman A, Kario D, Mussel M, Brill J, Buetow K, Efroni S, Nevo U. Cell studio: A platform for interactive, 3D graphical simulation of immunological processes. APL Bioeng 2018; 2:026107. [PMID: 31069304 PMCID: PMC6481718 DOI: 10.1063/1.5039473] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 05/04/2018] [Indexed: 12/27/2022] Open
Abstract
The field of computer modeling and simulation of biological systems is rapidly advancing, backed by significant progress in the fields of experimentation techniques, computer hardware, and programming software. The result of a simulation may be delivered in several ways, from numerical results, through graphs of the simulated run, to a visualization of the simulation. The vision of an in-silico experiment mimicking an in-vitro or in-vivo experiment as it is viewed under a microscope is appealing but technically demanding and computationally intensive. Here, we report “Cell Studio,” a generic, hybrid platform to simulate an immune microenvironment with biological and biophysical rules. We use game engines—generic programs for game creation which offer ready-made assets and tools—to create a visualized, interactive 3D simulation. We also utilize a scalable architecture that delegates the computational load to a server. The user may view the simulation, move the “camera” around, stop, fast-forward, and rewind it and inject soluble molecules into the extracellular medium at any point in time. During simulation, graphs are created in real time for a broad view of system-wide processes. The model is parametrized using a user-friendly Graphical User Interface (GUI). We show a simple validation simulation and compare its results with those from a “classical” simulation, validated against a “wet” experiment. We believe that interactive, real-time 3D visualization may aid in generating insights from the model and encourage intuition about the immunological scenario.
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Affiliation(s)
- Asaf Liberman
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | | | - Matan Mussel
- Physics Department, TU Dortmund University, Dortmund 44227, Germany
| | - Jacob Brill
- Arizona State University, Tempe, Arizona 85281, USA
| | | | - Sol Efroni
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan 52900, Israel
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16
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Developing a Policy Flight Simulator to Facilitate the Adoption of an Evidence-Based Intervention. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2018; 6:4800112. [PMID: 29805921 PMCID: PMC5957263 DOI: 10.1109/jtehm.2018.2833847] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 03/13/2018] [Accepted: 04/24/2018] [Indexed: 11/17/2022]
Abstract
While the use of evidence-based interventions (EBIs) has been advocated by the medical research community for quite some time, uptake of these interventions by healthcare providers has been slow. One possible explanation is that it is challenging for providers to estimate impacts of a specific EBI on their particular organization. To address that concern, we developed and evaluated a type of simulation called a policy flight simulator to determine if it could improve the adoption decision about a specific EBI, the transitional care model (TCM). The TCM uses an advanced practice nurse-led model of care to transition older adults with multiple chronic conditions from a hospitalization to home. An evaluation by a National Advisory Committee, made up of senior representatives from various stakeholders in the U.S. healthcare system, found the policy flight simulator to be a useful tool that has the potential to better inform adoption decisions. This paper describes the simulation development effort and documents lessons learned that may be useful to the healthcare modeling community and those interested in using simulation to support decisions based on EBIs.
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17
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Zun PS, Anikina T, Svitenkov A, Hoekstra AG. A Comparison of Fully-Coupled 3D In-Stent Restenosis Simulations to In-vivo Data. Front Physiol 2017; 8:284. [PMID: 28588498 PMCID: PMC5440556 DOI: 10.3389/fphys.2017.00284] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 04/19/2017] [Indexed: 01/05/2023] Open
Abstract
We describe our fully-coupled 3D multiscale model of in-stent restenosis, with blood flow simulations coupled to smooth muscle cell proliferation, and report results of numerical simulations performed with this model. This novel model is based on several previously reported 2D models. We study the effects of various parameters on the process of restenosis and compare with in vivo porcine data where we observe good qualitative agreement. We study the effects of stent deployment depth (and related injury score), reendothelization speed, and simulate the effect of stent width. Also we demonstrate that we are now capable to simulate restenosis in real-sized (18 mm long, 2.8 mm wide) vessel geometries.
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Affiliation(s)
- Pavel S. Zun
- Saint Petersburg State University of Information Technologies, Mechanics and Optics (ITMO) UniversitySt. Petersburg, Russia
- Computational Science Lab, Faculty of Science, Institute for Informatics, University of AmsterdamAmsterdam, Netherlands
| | - Tatiana Anikina
- Saint Petersburg State University of Information Technologies, Mechanics and Optics (ITMO) UniversitySt. Petersburg, Russia
- Computational Science Lab, Faculty of Science, Institute for Informatics, University of AmsterdamAmsterdam, Netherlands
| | - Andrew Svitenkov
- Saint Petersburg State University of Information Technologies, Mechanics and Optics (ITMO) UniversitySt. Petersburg, Russia
| | - Alfons G. Hoekstra
- Saint Petersburg State University of Information Technologies, Mechanics and Optics (ITMO) UniversitySt. Petersburg, Russia
- Computational Science Lab, Faculty of Science, Institute for Informatics, University of AmsterdamAmsterdam, Netherlands
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18
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Hoekstra AG, Alowayyed S, Lorenz E, Melnikova N, Mountrakis L, van Rooij B, Svitenkov A, Závodszky G, Zun P. Towards the virtual artery: a multiscale model for vascular physiology at the physics-chemistry-biology interface. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2016; 374:rsta.2016.0146. [PMID: 27698036 PMCID: PMC5052730 DOI: 10.1098/rsta.2016.0146] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/14/2016] [Indexed: 05/27/2023]
Abstract
This discussion paper introduces the concept of the Virtual Artery as a multiscale model for arterial physiology and pathologies at the physics-chemistry-biology (PCB) interface. The cellular level is identified as the mesoscopic level, and we argue that by coupling cell-based models with other relevant models on the macro- and microscale, a versatile model of arterial health and disease can be composed. We review the necessary ingredients, both models of arteries at many different scales, as well as generic methods to compose multiscale models. Next, we discuss how this can be combined into the virtual artery. Finally, we argue that the concept of models at the PCB interface could or perhaps should become a powerful paradigm, not only as in our case for studying physiology, but also for many other systems that have such PCB interfaces.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.
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Affiliation(s)
- Alfons G Hoekstra
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH Amsterdam, The Netherlands High Performance Computing Department, ITMO University, Saint Petersburg, Russia
| | - Saad Alowayyed
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH Amsterdam, The Netherlands King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
| | - Eric Lorenz
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH Amsterdam, The Netherlands Electric Ant Lab BV, Panamalaan 4 K, 1019AZ Amsterdam, The Netherlands
| | - Natalia Melnikova
- High Performance Computing Department, ITMO University, Saint Petersburg, Russia
| | - Lampros Mountrakis
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH Amsterdam, The Netherlands
| | - Britt van Rooij
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH Amsterdam, The Netherlands
| | - Andrew Svitenkov
- High Performance Computing Department, ITMO University, Saint Petersburg, Russia
| | - Gábor Závodszky
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH Amsterdam, The Netherlands
| | - Pavel Zun
- High Performance Computing Department, ITMO University, Saint Petersburg, Russia
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Mizeranschi A, Groen D, Borgdorff J, Hoekstra AG, Chopard B, Dubitzky W. Anatomy and Physiology of Multiscale Modeling and Simulation in Systems Medicine. Methods Mol Biol 2016; 1386:375-404. [PMID: 26677192 DOI: 10.1007/978-1-4939-3283-2_17] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Systems medicine is the application of systems biology concepts, methods, and tools to medical research and practice. It aims to integrate data and knowledge from different disciplines into biomedical models and simulations for the understanding, prevention, cure, and management of complex diseases. Complex diseases arise from the interactions among disease-influencing factors across multiple levels of biological organization from the environment to molecules. To tackle the enormous challenges posed by complex diseases, we need a modeling and simulation framework capable of capturing and integrating information originating from multiple spatiotemporal and organizational scales. Multiscale modeling and simulation in systems medicine is an emerging methodology and discipline that has already demonstrated its potential in becoming this framework. The aim of this chapter is to present some of the main concepts, requirements, and challenges of multiscale modeling and simulation in systems medicine.
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Affiliation(s)
- Alexandru Mizeranschi
- Biomedical Sciences Research Institute, University of Ulster, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK
| | - Derek Groen
- Chemistry Department, Centre for Computational Science, University College London, 20 Gordon Street, WC1H 0AJ, London, UK
| | - Joris Borgdorff
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Alfons G Hoekstra
- Computational Science Lab, Institute for Informatics, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH, Amsterdam, The Netherlands
- Advanced Computing Lab, ITMO University, 197101, 49 Kronverkskiy av., St. Petersburg, Russia
| | - Bastien Chopard
- Computer Science Department, University of Geneva, 7 route de Drize, 1227, Carouge, Switzerland
| | - Werner Dubitzky
- Biomedical Sciences Research Institute, University of Ulster, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK.
- School of Biomedical Sciences, University of Ulster, Coleraine campus, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK.
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Knap J, Spear CE, Borodin O, Leiter KW. Advancing a distributed multi-scale computing framework for large-scale high-throughput discovery in materials science. NANOTECHNOLOGY 2015; 26:434004. [PMID: 26443333 DOI: 10.1088/0957-4484/26/43/434004] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We describe the development of a large-scale high-throughput application for discovery in materials science. Our point of departure is a computational framework for distributed multi-scale computation. We augment the original framework with a specialized module whose role is to route evaluation requests needed by the high-throughput application to a collection of available computational resources. We evaluate the feasibility and performance of the resulting high-throughput computational framework by carrying out a high-throughput study of battery solvents. Our results indicate that distributed multi-scale computing, by virtue of its adaptive nature, is particularly well-suited for building high-throughput applications.
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Affiliation(s)
- J Knap
- Simulation Sciences Branch, RDRL-CIH-C, US Army Research Laboratory, Aberdeen Proving Ground, MD, 21005-5066, USA
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21
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Cappuccio A, Tieri P, Castiglione F. Multiscale modelling in immunology: a review. Brief Bioinform 2015; 17:408-18. [PMID: 25810307 DOI: 10.1093/bib/bbv012] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 01/30/2015] [Indexed: 01/26/2023] Open
Abstract
One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.
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Affiliation(s)
- Antonio Cappuccio
- Laboratory of Integrative biology of human dendritic cells and T cells, U932 Immunity and cancer, Institut Curie, 26 Rue d`Ulm, 75005 Paris, France
| | - Paolo Tieri
- Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy
| | - Filippo Castiglione
- Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy
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Karabasov S, Nerukh D, Hoekstra A, Chopard B, Coveney PV. Multiscale modelling: approaches and challenges. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2014; 372:rsta.2013.0390. [PMID: 24982248 PMCID: PMC4084530 DOI: 10.1098/rsta.2013.0390] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Affiliation(s)
- Sergey Karabasov
- Department of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | - Dmitry Nerukh
- Department of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK
| | - Alfons Hoekstra
- Computational Science, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands National Research University ITMO, Kronverkskiy Prospekt 49, 197101 St Petersburg, Russia
| | - Bastien Chopard
- Computer Science Department, University of Geneva, 1211 Geneva 4, Switzerland
| | - Peter V Coveney
- Centre for Computational Science, University College London, 20 Gordon Street, London WC1H OAJ, UK
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23
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Hoekstra A, Chopard B, Coveney P. Multiscale modelling and simulation: a position paper. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A: MATHEMATICAL, PHYSICAL AND ENGINEERING SCIENCES 2014; 372:rsta.2013.0377. [PMID: 24982256 DOI: 10.1098/rsta.2013.0377] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
We argue that, despite the fact that the field of multiscale modelling and simulation has enjoyed significant success within the past decade, it still holds many open questions that are deemed important but so far have barely been explored. We believe that this is at least in part due to the fact that the field has been mainly developed within disciplinary silos. The principal topics that in our view would benefit from a targeted
multidisciplinary
research effort are related to reaching consensus as to what exactly one means by ‘multiscale modelling’, formulating a generic theory or calculus of multiscale modelling, applying such concepts to the urgent question of validation and verification of multiscale models, and the issue of numerical error propagation in multiscale models. Moreover, we believe that this would, in principle, also lay the foundation for more efficient, well-defined and usable multiscale computing environments. We believe that multidisciplinary research to fill in the gaps is timely, highly relevant, and with substantial potential impact on many scientific disciplines.
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Affiliation(s)
- Alfons Hoekstra
- Computational Science, Institute for Informatics, Faculty of Science, University of Amsterdam, The Netherlands
- National Research University ITMO, St Petersburg, Russian Federation
| | | | - Peter Coveney
- Centre for Computational Science, University College London, London, UK
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Borgdorff J, Ben Belgacem M, Bona-Casas C, Fazendeiro L, Groen D, Hoenen O, Mizeranschi A, Suter JL, Coster D, Coveney PV, Dubitzky W, Hoekstra AG, Strand P, Chopard B. Performance of distributed multiscale simulations. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2014; 372:rsta.2013.0407. [PMID: 24982258 PMCID: PMC4084531 DOI: 10.1098/rsta.2013.0407] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Multiscale simulations model phenomena across natural scales using monolithic or component-based code, running on local or distributed resources. In this work, we investigate the performance of distributed multiscale computing of component-based models, guided by six multiscale applications with different characteristics and from several disciplines. Three modes of distributed multiscale computing are identified: supplementing local dependencies with large-scale resources, load distribution over multiple resources, and load balancing of small- and large-scale resources. We find that the first mode has the apparent benefit of increasing simulation speed, and the second mode can increase simulation speed if local resources are limited. Depending on resource reservation and model coupling topology, the third mode may result in a reduction of resource consumption.
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Affiliation(s)
- J Borgdorff
- Computational Science, Informatics Institute, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - M Ben Belgacem
- Computer Science Department, University of Geneva, 1227 Carouge, Switzerland
| | - C Bona-Casas
- Department of Applied Mathematics, University of A Coruña, 15001 A Coruña, Spain
| | - L Fazendeiro
- Department of Earth and Space Sciences, Chalmers University of Technology, 41296 Göteborg, Sweden
| | - D Groen
- Centre for Computational Science, University College London, 20 Gordon Street, London WC1H OAJ, UK
| | - O Hoenen
- Max-Planck-Institut für Plasmaphysik, 85748 Garching, Germany
| | - A Mizeranschi
- Nano Systems Biology, School of Biomedicine, University of Ulster, Coleraine BTS2 1SA, UK
| | - J L Suter
- Centre for Computational Science, University College London, 20 Gordon Street, London WC1H OAJ, UK
| | - D Coster
- Max-Planck-Institut für Plasmaphysik, 85748 Garching, Germany
| | - P V Coveney
- Centre for Computational Science, University College London, 20 Gordon Street, London WC1H OAJ, UK
| | - W Dubitzky
- Nano Systems Biology, School of Biomedicine, University of Ulster, Coleraine BTS2 1SA, UK
| | - A G Hoekstra
- Computational Science, Informatics Institute, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands National Research University ITMO, Kronverkskiy prospekt 49, 197101 St Petersburg, Russia
| | - P Strand
- Department of Earth and Space Sciences, Chalmers University of Technology, 41296 Göteborg, Sweden
| | - B Chopard
- Computer Science Department, University of Geneva, 1227 Carouge, Switzerland
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