A multiphysics and multiscale software environment for modeling astrophysical systems

We present MUSE, a software framework for combining existing computational tools for different astrophysical domains into a single multiphysics, multiscale application. MUSE facilitates the coupling of existing codes written in different languages by providing inter-language tools and by specifying an interface between each module and the framework that represents a balance between generality and computational efficiency. This approach allows scientists to use combinations of codes to solve highly-coupled problems without the need to write new codes for other domains or significantly alter their existing codes. MUSE currently incorporates the domains of stellar dynamics, stellar evolution and stellar hydrodynamics for studying generalized stellar systems. We have now reached a "Noah's Ark" milestone, with (at least) two available numerical solvers for each domain. MUSE can treat multi-scale and multi-physics systems in which the time- and size-scales are well separated, like simulating the evolution of planetary systems, small stellar associations, dense stellar clusters, galaxies and galactic nuclei. In this paper we describe three examples calculated using MUSE: the merger of two galaxies, the merger of two evolving stars, and a hybrid N-body simulation. In addition, we demonstrate an implementation of MUSE on a distributed computer which may also include special-purpose hardware, such as GRAPEs or GPUs, to accelerate computations. The current MUSE code base is publicly available as open source at http://muse.liComment: 24 pages, To appear in New Astronomy Source code available at http://muse.l

Towards Distributed Petascale Computing

In this chapter we will argue that studying such multi-scale multi-science systems gives rise to inherently hybrid models containing many different algorithms best serviced by different types of computing environments (ranging from massively parallel computers, via large-scale special purpose machines to clusters of PC's) whose total integrated computing capacity can easily reach the PFlop/s scale. Such hybrid models, in combination with the by now inherently distributed nature of the data on which the models `feed' suggest a distributed computing model, where parts of the multi-scale multi-science model are executed on the most suitable computing environment, and/or where the computations are carried out close to the required data (i.e. bring the computations to the data instead of the other way around). We presents an estimate for the compute requirements to simulate the Galaxy as a typical example of a multi-scale multi-physics application, requiring distributed Petaflop/s computational power.Comment: To appear in D. Bader (Ed.) Petascale, Computing: Algorithms and Applications, Chapman & Hall / CRC Press, Taylor and Francis Grou

Ein verteilter und agentenbasierter Ansatz für gekoppelte Probleme der rechnergestützten Ingenieurwissenschaften

Challenging questions in science and engineering often require to decouple a complex problem and to focus on isolated sub-problems first. The knowledge of those individual solutions can later be combined to obtain the result for the full question. A similar technique is applied in numerical modeling. Here, the software solver for subsets of the coupled problem might already exist and can directly be used. This thesis describes a software environment capable of combining multiple software solvers, the result being a new, combined model. Two important design decisions were crucial at the beginning: First, every sub-model keeps full control of its execution. Second, the source code of the sub-model requires only minimal adaptation. The sub-models choose themselves when to issue communication calls, with no outer synchronisation mechanism required. The coupling of heterogeneous hardware is supported as well as the use of homogeneous compute clusters. Furthermore, the coupling framework allows sub-solvers to be written in different programming languages. Also, each of the sub-models may operate on its own spatial and temporal scales. The next challenge was to allow the potential coupling of thousands software agents, being able to utilise today's petascale hardware. For this purpose, a specific coupling framework was designed and implemented, combining the experiences from the previous work with additions required to cope with the targeted number of coupled sub-models. The large number of interacting models required a much more dynamic approach, where the agents automatically detect their communication partners at runtime. This eliminates the need to explicitly specify the coupling graph a~priori. Agents are allowed to enter (and leave) the simulation at any time, with the coupling graph changing accordingly.Da viele Problemstellungen im Ingenieurwesen sehr komplex sind, ist es oft sinnvoll, sie in einzelne Teilprobleme aufzugliedern. Diese Teilbereiche können nun einzeln angegangen und dann zur Gesamtlösung kombiniert werden. Ein ähnlicher Ansatz wird bei der numerischen Modellierung verfolgt: Komplexe Software wird schrittweise erstellt, indem Software-Löser für einzelne Bereiche zuerst separat erarbeitet werden. In dieser Arbeit wird eine Software beschrieben, die eine Vielzahl von unabhängigen Software-Lösern kombinieren kann. Jedes Teilmodell verhält sich weiterhin wie ein selbständiges Programm. Hierfür wird es in einen Software-Agenten gehüllt. Zur Kopplung sind lediglich minimale Ergänzungen am Quellcode des Teilmodells nötig. Möglich wird dies durch die Struktur der Kommunikation zwischen den Teilmodellen. Sie lässt den Modellen die Kontrolle über die Kommunikationsaufrufe und benötigt zur Synchronisation keine Einflussnahme einer übergeordneten Instanz. Manche Teilmodelle sind für den Gebrauch mit einer speziellen Hardware optimiert. Daher musste das Zusammenspiel unterschiedlicher Hardware ebenso berücksichtigt werden wie homogene Rechencluster. Weiterhin ermöglicht das Kopplungs-Framework, dass unterschiedliche Programmiersprachen verbunden werden können. Wie schon der Programmablauf, so können auch die Modellparameter, etwa die räumliche und zeitliche Skala, von Teilmodell zu Teilmodell unterschiedlich bleiben. Weiter behandelt diese Arbeit eine Vorgehensweise um tausende von Software-Agenten zu einem Groß-Modell zu koppeln. Dies ist erforderlich, wenn die Ressourcen heutiger Petascale Rechencluster benutzt werden sollen. Hierzu wurde das bisherige Framework neu aufgelegt, da die große Anzahl von zu koppelnden Modellen einer wesentlich dynamischeren Kommunikationsstruktur bedarf. Die Agenten der Teilmodelle können einer laufenden Simulation hinzugefügt werden (oder diese verlassen) und die globalen Kopplungsbeziehungen passen sich dementsprechend an

Support for multiscale simulations with molecular dynamics

We present a reusable solution that supports users in combining single-scale models to create a multiscale application. Our approach applies several multiscale programming tools to allow users to compose multiscale applications using a graphical interface, and provides an easy way to execute these multiscale applications on international production infrastructures. Our solution extends the general purpose scripting approach of the GridSpace platform with simple mechanisms for accessing production resources, provided by the Application Hosting Environment (AHE). We apply our support solution to construct and execute a multiscale simulation of clay-polymer nanocomposite materials, and showcase its benefit in reducing the effort required to do a number of time-intensive user tasks. © 2013 The Authors. Published by Elsevier B.V

Orchestration of multiscale model for computational oncology

Cancer is a challenging disease that involves multiple types of biological interactions in different time and space scales. Often computational modelling has been facing problems that, in the current technology level, is impracticable to represent in a single space-time continuum. To handle this sort of problems, complex orchestrations of multiscale models is frequently done. PRIMAGE is a large EU project that aims to support personalized childhood cancer diagnosis and prognosis. The goal is to do so predicting the growth of the solid tumour using multiscale in-silico technologies. The project proposes an open cloud-based platform to support decision making in the clinical management of paediatric cancers. The orchestration of predictive models is in general complex and would require a software framework that support and facilitate such task. The present work, proposes the development of an updated framework, referred herein as the VPH-HFv3, as a part of the PRIMAGE project. This framework, a complete re-writing with respect to the previous versions, aims to orchestrate several models, which are in concurrent development, using an architecture as simple as possible, easy to maintain and with high reusability. This sort of problem generally requires unfeasible execution times. To overcome this problem was developed a strategy of particularisation, which maps the upper-scale model results into a smaller number and homogenisation which does the inverse way and analysed the accuracy of this approach