08 Fakultät Mathematik und Physik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/9

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Institute: search.filters.UBSInstitut.Institut für Angewandte Analysis und numerische SimulationSubject: search.filters.subject.004

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    Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction
    (2021) Benacchio, Tommaso; Bonaventura, Luca; Altenbernd, Mirco; Cantwell, Chris D.; Düben, Peter D.; Gillard, Mike; Giraud, Luc; Göddeke, Dominik; Raffin, Erwan; Teranishi, Keita; Wedi, Nils
    Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.
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    Zeitparallele Mehrgittermethoden für die Simulation des neuromuskulären Systems
    (2020) Nitzsche, Marius
    In dieser Arbeit wird das „Multigrid Reduction in Time“ Verfahren vorgestellt und auf die Simulation des neuromuskulären Systems angewendet, um durch zusätzliche Zeitparallelisierung das sehr umfangreiche Differentialgleichungssystem mit entsprechenden Hochleistungsrechnern in kürzerer Zeit zu lösen. Dazu werden geeignete Parametereinstellungen bezüglich des Vergröberungsfaktors, der Relaxation und des Zyklus' bestimmt. Dabei wird festgestellt, dass für hohe Ortsauflösungen entsprechend kleine Zeitschrittweiten des expliziten Lösers gewählt werden müssen, da sonst MGRIT divergiert. Außerdem wird die Skalierung des zeitparallelen Verfahrens untersucht. Es stellt sich heraus, dass MGRIT mit dem hier präsentierten Aufbau des Grobgitteroperators, welcher aus Gründen der Stabilität gewählt wird, eine nicht so gute Skalierung wie bei der Anwendung auf das Referenzproblem vorweist. Es kann eine Laufzeitsenkung gegenüber des in der Zeit sequentiellen Lösungsverfahrens erreicht werden, die allerdings wegen der schlechteren Skalierung geringer als erwartet ausfällt.
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    Knowledge-based modeling of simulation behavior for Bayesian optimization
    (2024) Huber, Felix; Bürkner, Paul-Christian; Göddeke, Dominik; Schulte, Miriam
    Numerical simulations consist of many components that affect the simulation accuracy and the required computational resources. However, finding an optimal combination of components and their parameters under constraints can be a difficult, time-consuming and often manual process. Classical adaptivity does not fully solve the problem, as it comes with significant implementation cost and is difficult to expand to multi-dimensional parameter spaces. Also, many existing data-based optimization approaches treat the optimization problem as a black-box, thus requiring a large amount of data. We present a constrained, model-based Bayesian optimization approach that avoids black-box models by leveraging existing knowledge about the simulation components and properties of the simulation behavior. The main focus of this paper is on the stochastic modeling ansatz for simulation error and run time as optimization objective and constraint, respectively. To account for data covering multiple orders of magnitude, our approach operates on a logarithmic scale. The models use a priori knowledge of the simulation components such as convergence orders and run time estimates. Together with suitable priors for the model parameters, the model is able to make accurate predictions of the simulation behavior. Reliably modeling the simulation behavior yields a fast optimization procedure because it enables the optimizer to quickly indicate promising parameter values. We test our approach experimentally using the multi-scale muscle simulation framework OpenDiHu and show that we successfully optimize the time step widths in a time splitting approach in terms of minimizing the overall error under run time constraints.