109 research outputs found
A distributed multiscale computation of a tightly coupled model using the Multiscale Modeling Language
AbstractNature is observed at all scales; with multiscale modeling, scientists bring together several scales for a holistic analysis of a phenomenon. The models on these different scales may require significant but also heterogeneous computational resources, creating the need for distributed multiscale computing. A particularly demanding type of multiscale models, tightly coupled, brings with it a number of theoretical and practical issues. In this contribution, a tightly coupled model of in-stent restenosis is first theoretically examined for its multiscale merits using the Multiscale Modeling Language (MML); this is aided by a toolchain consisting of MAPPER Memory (MaMe), the Multiscale Application Designer (MAD), and Gridspace Experiment Workbench. It is implemented and executed with the general Multiscale Coupling Library and Environment (MUSCLE). Finally, it is scheduled amongst heterogeneous infrastructures using the QCG-Broker. This marks the first occasion that a tightly coupled application uses distributed multiscale computing in such a general way
Survey of multiscale and multiphysics applications and communities
Multiscale and multiphysics applications are now commonplace, and many researchers focus on combining existing models to construct new multiscale models. This concise review of multiscale applications and their source communities in the EU and US outlines differences and commonalities among approaches and identifies areas in which collaboration between disciplines could be particularly beneficial. Because different communities adopt very different approaches to constructing multiscale simulations, and simulations on a length scale of a few meters and a time scale of a few hours can be found in many multiscale research domains, communities might receive additional benefit from sharing methods that are geared towards these scales. The Web extra is the full literature list mentioned in the article.This work has received funding from the MAPPER EC-FP7 project (grant no. RI-261507)
Multiscale computing with the multiscale modeling library and runtime environment
We introduce a software tool to simulate multiscale models: The Multiscale Coupling Library and Environment 2 (MUSCLE 2). MUSCLE 2 is a component-based modeling tool inspired by the multiscale modeling and simulation framework, with an easy-to-use API which supports Java, C++, C, and Fortran. We present MUSCLE 2's runtime features, such as its distributed computing capabilities, and its benefits to multiscale modelers. We also describe two multiscale models that use MUSCLE 2 to do distributed multiscale computing: An in-stent restenosis and a canal system model. We conclude that MUSCLE 2 is a notable improvement over the previous version of MUSCLE, and that it allows users to more flexibly deploy simulations of multiscale models, while improving their performance. © 2013 The Authors. Published by Elsevier B.V
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Semantically Linking In Silico Cancer Models
Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible
VPH-HF: A software framework for the execution of complex subject-specific physiology modelling workflows
Computational medicine more and more requires complex orchestrations of multiple modelling & simulation codes, written in different programming languages and with different computational requirements, which when validated need to be run many times on large cohorts of patients. The aim of this paper is to present a new open source software, the VPH Hypermodelling Framework (VPH-HF). The VPH-HF overcomes the limitations of most workflow execution environments by supporting both Taverna and Muscle2; the addition of Muscle2 support makes possible the execution of very complex orchestrations that include strongly-coupled models. The overhead that the VPH-HF imposes in exchange for this is small, and tends to be flat regardless of the complexity and the computational cost of the hypermodel being executed. We recommend the use of the VPH-HF to orchestrate any hypermodel with an execution time of 200 s or higher, which would confine the VPH-HF overhead to less than 10%. The VPH-HF also provide an automatic caching system over the execution of every hypomodel, which may provide considerable speed-up when the orchestration is run repeatedly over large numbers of patients or within stochastic frameworks, and the input sets are properly binned. The caching system also makes it easy to form large input set/output set databases required to develop reduced-order models, and the framework offers the possibility to dynamically replace single models in the orchestration with reduced-order versions built from cached results, an essential feature when the orchestration of multiple models produces a combinatory explosion of the computational cost
SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed Networks
Signed network structure discovery has received extensive attention and has
become a research focus in the field of network science. However, most of the
existing studies are focused on the networks with a single structure, e.g.,
community or bipartite, while ignoring multiple structures, e.g., the
coexistence of community and bipartite structures. Furthermore, existing
studies were faced with challenge regarding large-scale signed networks due to
their high time complexity, especially when determining the number of clusters
in the observed network without any prior knowledge. In view of this, we
propose a mathematically principled method for signed network multiple
structure discovery named the Signed Stochastic Block Model (SSBM). The SSBM
can capture the multiple structures contained in signed networks, e.g.,
community, bipartite, and coexistence of them, by adopting a probabilistic
model. Moreover, by integrating the minimum message length (MML) criterion and
component-wise EM (CEM) algorithm, a scalable learning algorithm that has the
ability of model selection is proposed to handle large-scale signed networks.
By comparing state-of-the-art methods on synthetic and real-world signed
networks, extensive experimental results demonstrate the effectiveness and
efficiency of SSBM in discovering large-scale exploratory signed networks with
multiple structures
Dagstuhl News January - December 2007
"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic
Toward a formal theory for computing machines made out of whatever physics offers: extended version
Approaching limitations of digital computing technologies have spurred
research in neuromorphic and other unconventional approaches to computing. Here
we argue that if we want to systematically engineer computing systems that are
based on unconventional physical effects, we need guidance from a formal theory
that is different from the symbolic-algorithmic theory of today's computer
science textbooks. We propose a general strategy for developing such a theory,
and within that general view, a specific approach that we call "fluent
computing". In contrast to Turing, who modeled computing processes from a
top-down perspective as symbolic reasoning, we adopt the scientific paradigm of
physics and model physical computing systems bottom-up by formalizing what can
ultimately be measured in any physical substrate. This leads to an
understanding of computing as the structuring of processes, while classical
models of computing systems describe the processing of structures.Comment: 76 pages. This is an extended version of a perspective article with
the same title that will appear in Nature Communications soon after this
manuscript goes public on arxi
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