13 Zentrale Universitätseinrichtungen
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/14
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Item Open Access Performance comparison of CFD microbenchmarks on diverse HPC architectures(2024) Galeazzo, Flavio C. C.; Garcia-Gasulla, Marta; Boella, Elisabetta; Pocurull, Josep; Lesnik, Sergey; Rusche, Henrik; Bnà, Simone; Cerminara, Matteo; Brogi, Federico; Marchetti, Filippo; Gregori, Daniele; Weiß, R. Gregor; Ruopp, AndreasOpenFOAM is a CFD software widely used in both industry and academia. The exaFOAM project aims at enhancing the HPC scalability of OpenFOAM, while identifying its current bottlenecks and proposing ways to overcome them. For the assessment of the software components and the code profiling during the code development, lightweight but significant benchmarks should be used. The answer was to develop microbenchmarks, with a small memory footprint and short runtime. The name microbenchmark does not mean that they have been prepared to be the smallest possible test cases, as they have been developed to fit in a compute node, which usually has dozens of compute cores. The microbenchmarks cover a broad band of applications: incompressible and compressible flow, combustion, viscoelastic flow and adjoint optimization. All benchmarks are part of the OpenFOAM HPC Technical Committee repository and are fully accessible. The performance using HPC systems with Intel and AMD processors (x86_64 architecture) and Arm processors (aarch64 architecture) have been benchmarked. For the workloads in this study, the mean performance with the AMD CPU is 62% higher than with Arm and 42% higher than with Intel. The AMD processor seems particularly suited resulting in an overall shorter time-to-solution.Item Open Access Soya yield prediction on a within-field scale using machine learning models trained on Sentinel-2 and soil data(2022) Pejak, Branislav; Lugonja, Predrag; Antić, Aleksandar; Panić, Marko; Pandžić, Miloš; Alexakis, Emmanouil; Mavrepis, Philip; Zhou, Naweiluo; Marko, Oskar; Crnojević, VladimirAgriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA’s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%.Item Open Access Coherent mesh representation for parallel I/O of unstructured polyhedral meshes(2024) Weiß, R. Gregor; Lesnik, Sergey; Galeazzo, Flavio C. C.; Ruopp, Andreas; Rusche, HenrikThis paper presents a new mesh data layout for parallel I/O of linear unstructured polyhedral meshes. The new mesh representation infers coherence across entities of different topological dimensions, i.e., grid cells, faces, and points. The coherence due to cell-to-face and face-to-point connectivities of the mesh is formulated as a tree data structure distributed across processors. The mesh distribution across processors creates consecutive and contiguous slices that render an optimized data access pattern for parallel I/O. A file format using the coherent mesh representation, developed and tested with OpenFOAM, enables the usability of the software at unprecedented scales. Further implications of the coherent and sliceable mesh representation arise due to simplifications in partitioning and diminished pre- and post-processing overheads.Item Open Access On error-based step size control for discontinuous Galerkin methods for compressible fluid dynamics(2023) Ranocha, Hendrik; Winters, Andrew R.; Castro, Hugo Guillermo; Dalcin, Lisandro; Schlottke-Lakemper, Michael; Gassner, Gregor; Parsani, MatteoWe study a temporal step size control of explicit Runge-Kutta (RK) methods for compressible computational fluid dynamics (CFD), including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler equations. We demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy (CFL) number. Our numerical examples show that the error-based step size control is easy to use, robust, and efficient, e.g., for (initial) transient periods, complex geometries, nonlinear shock capturing approaches, and schemes that use nonlinear entropy projections. We demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases, the open source Julia packages Trixi.jl with OrdinaryDiffEq.jl and the C/Fortran code SSDC based on PETSc.