Mathematical Software

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New submissions for Fri, 10 May 24

[1]  arXiv:2405.05640 [pdf, other]

Title: Experience and Analysis of Scalable High-Fidelity Computational Fluid Dynamics on Modular Supercomputing Architectures

Authors: Martin Karp, Estela Suarez, Jan H. Meinke, Måns I. Andersson, Philipp Schlatter, Stefano Markidis, Niclas Jansson

Comments: 13 pages, 5 figures, 3 tables, preprint

Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Mathematical Software (cs.MS); Fluid Dynamics (physics.flu-dyn)

The never-ending computational demand from simulations of turbulence makes computational fluid dynamics (CFD) a prime application use case for current and future exascale systems. High-order finite element methods, such as the spectral element method, have been gaining traction as they offer high performance on both multicore CPUs and modern GPU-based accelerators. In this work, we assess how high-fidelity CFD using the spectral element method can exploit the modular supercomputing architecture at scale through domain partitioning, where the computational domain is split between a Booster module powered by GPUs and a Cluster module with conventional CPU nodes. We investigate several different flow cases and computer systems based on the modular supercomputing architecture (MSA). We observe that for our simulations, the communication overhead and load balancing issues incurred by incorporating different computing architectures are seldom worthwhile, especially when I/O is also considered, but when the simulation at hand requires more than the combined global memory on the GPUs, utilizing additional CPUs to increase the available memory can be fruitful. We support our results with a simple performance model to assess when running across modules might be beneficial. As MSA is becoming more widespread and efforts to increase system utilization are growing more important our results give insight into when and how a monolithic application can utilize and spread out to more than one module and obtain a faster time to solution.

Replacements for Fri, 10 May 24

[2]  arXiv:2402.13768 (replaced) [pdf, other]

Title: Democratizing Uncertainty Quantification

Authors: Linus Seelinger, Anne Reinarz, Mikkel B. Lykkegaard, Robert Akers, Amal M. A. Alghamdi, David Aristoff, Wolfgang Bangerth, Jean Bénézech, Matteo Diez, Kurt Frey, John D. Jakeman, Jakob S. Jørgensen, Ki-Tae Kim, Massimiliano Martinelli, Matthew Parno, Riccardo Pellegrini, Noemi Petra, Nicolai A. B. Riis, Katherine Rosenfeld, Andrea Serani, Lorenzo Tamellini, Umberto Villa, Tim J. Dodwell, Robert Scheichl

Comments: Fix broken reference, add R. Akers as author for contribution to tritium benchmark

Subjects: Mathematical Software (cs.MS); Applications (stat.AP)

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