Accelerating the FlowSimulator: Node-Level Performance Analysis of High-Performance CFD Solvers using LIKWID

Johannes Wendler (), Jana Gericke-Schuster (), Marco Cristofaro (), Neda Ebrahimi Pour () and Immo Huismann ()
Additional contact information
Johannes Wendler: German Aerospace Center (DLR), Institute of Software Methods for Product Virtualization
Jana Gericke-Schuster: German Aerospace Center (DLR), Institute of Software Methods for Product Virtualization
Marco Cristofaro: German Aerospace Center (DLR), Institute of Software Methods for Product Virtualization
Neda Ebrahimi Pour: German Aerospace Center (DLR), Institute of Software Methods for Product Virtualization
Immo Huismann: German Aerospace Center (DLR), Institute of Software Methods for Product Virtualization

A chapter in Tools for High Performance Computing 2023, 2026, pp 71-89 from Springer

Abstract: Abstract To achieve the best possible time to solution in High Performance Computing codes, node-level performance is a key factor. Thus, this performance analysis focuses on the two computational fluid dynamics (CFD) solvers CODA (Finite Volume) and Musubi (Lattice Boltzmann) on the DLR cluster CARO. We identify the relevant kernels of our software using profiling and tracing with Score-P and take a deeper look into their performance using the hardware performance monitoring tool LIKWID. Depending on the solver, different metrics are applied (e.g. roofline, CPI, cache misses, MLUP/s). Finally, we analyze the results in detail and derive possible optimization strategies for CODA and Musubi and summarize our experiences and best practices with the employed tools.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16397-4_5

Ordering information: This item can be ordered from
http://www.springer.com/9783032163974

DOI: 10.1007/978-3-032-16397-4_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().