A Distributed Multiscale Computation of a Tightly Coupled Model Using the Multiscale Modeling Language

Borgdorff, Joris; Bona-Casas, Carles; Mamonski, Mariusz; Kurowski, Krzysztof; Piontek, Tomasz; Bosak, Bartosz; Rycerz, Katarzyna; Ciepiela, Eryk; Gubała, Tomasz; Harężlak, Daniel; Bubak, Marian; Lorenz, Eric; Hoekstra, Alfons G.

doi:10.1016/j.procs.2012.04.064

Cited by 26 publications

(35 citation statements)

References 10 publications

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“…In other domains, tightly coupled multiscale models have been successfully tackled using frameworks such as the MUSCLE computing environment [18,19] combined with a domain specific Multiscale Modeling Language (MML) [20]. In MML [20], the multiscale model architecture is analyzed using directed acyclic task graphs, that facilitate validity checking and task scheduling, and a formal multiscale model and its pertinent computational architecture are specified in a modular fashion.…”

Section: Related Workmentioning

confidence: 99%

Multiscale Simulation of Organic Electronics Via Smart Scheduling of Quantum Mechanics Computations

Friederich

Strunk²,

Wenzel

et al. 2016

Procedia Computer Science

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Simulation of charge transport in disordered organic materials requires a huge number of quantum mechanical calculations and becomes particularly challenging when the polaron effect is explicitly included, i.e. the influence of the electrostatic environment of the molecules on the energy disorder. The polaron model gives rise to tasks of varying resource footprints and to dependencies between a large number of tasks. We solve the resulting tightly coupled multiscale model using the quantum patch approach by accounting for the dependencies arising from the self-consistency loops for constructing the workflow and applying a specific scheduling strategy for different task types. Our implementation of the method fully exploits the parallelism of the multiscale model alleviating the effects of load imbalance and dependencies so that it can be efficiently used on high performance computing machines.

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Section: Related Workmentioning

confidence: 99%

Multiscale Simulation of Organic Electronics Via Smart Scheduling of Quantum Mechanics Computations

Friederich

Strunk²,

Wenzel

et al. 2016

Procedia Computer Science

View full text Add to dashboard Cite

show abstract

“…GridSpace itself does not deal with lowlevel resource usage issues (such as reducing waiting time in a job queue); it provides a unified interface to resource access which includes not only simple ssh-based access, but also advanced external services supporting resource co-allocation and reservation. An example of usage of such services for a tightly coupled simulation is described in [26].…”

Section: Building and Running Multiscale Applicationsmentioning

confidence: 99%

“…and usage of various scientific software packages (LAMMPS and MUSCLE). However, the presented solution is used in many other applications, results are presented in separate papers: biomedical simulation [26], Irrigation Canals Simulation [30,31], Nanomaterials simulation [32].…”

Section: Test Application Case Studymentioning

confidence: 99%

“…Thanks to their usage, it is easier for application developers to achieve scientific results. One example is the physiology application described in [26]. The usage of tools for simulation of irrigation canals [30,31] was used as a basis for a tutorial used during first seasonal MAPPER school in London.…”

Section: Tools With Real-world Applicationsmentioning

confidence: 99%

“…The usage of tools for simulation of irrigation canals [30,31] was used as a basis for a tutorial used during first seasonal MAPPER school in London. 26 Tools were also applied in the simulation of clay-polymer nanocomposites (Nano-material science) [32], reverse engineering of gene-regulatory networks (Computational Biology), equilibrium-stability and in transport turbulence equilibrium workflows (Fusion modeling). At present 43 single-scale models and 38 mappers are already registered in the model registry (MaMe), representing almost all MAPPER applications in addition to a test application.…”

Section: Tools With Real-world Applicationsmentioning

confidence: 99%

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