Boosting the performance of computational fluid dynamics codes for interactive supercomputing

Paul R. Woodward, Jagan Jayaraj, Pei Hung Lin, Pen Chung Yew, Michael Knox, Jim Greensky, Anthony Nowatski, Karl Stoffels

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


An extreme form of pipelining of the Piecewise-Parabolic Method (PPM) gas dynamics code has been used to dramatically increase its performance on the new generation of multicore CPUs. Exploiting this technique, together with a full integration of the several data post-processing and visualization utilities associated with this code has enabled numerical experiments in computational fluid dynamics to be performed interactively on a new, dedicated system in our lab, with immediate, user controlled visualization of the resulting flows on the PowerWall display. The code restructuring required to achieve the necessary CPU performance boost, as well as the parallel computing methods and systems used to enable interactive flow simulation are described. Requirements for these techniques to be applied to other codes are discussed, and our plans for tools that will assist programmers to exploit these techniques are briefly described. Examples showing the capability of the new system and software are given for applications in turbulence and stellar convection.

Original languageEnglish (US)
Pages (from-to)2055-2064
Number of pages10
JournalProcedia Computer Science
Issue number1
StatePublished - 2010

Bibliographical note

Funding Information:
The construction and development of our interactive supercomputing system at the LCSE has been supported by an NSF Computer Research Infrastructure grant, CNS-0708822. Our work in interactive supercomputing has also been supported in part through the Minnesota Supercomputing Institute. Research work reported here as well as the development of our multifluid PPM gas dynamics code and translation tools for the Cell processor and Roadrunner system has been supported through a contract from the Los Alamos National Laboratory to the University of Minnesota.


  • Computational fluid dynamics
  • Interactive simulation and visualization
  • Programming for multicore processors


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