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Parallel Optimisation Strategies for Fusion Codes

Adrian Jackson, Fiona Reid, Stephen Booth, Joachim Hein, Jan Westerholm, Mats Aspnäs, Miquel Català, Alejandro Soba, Parallel Optimisation Strategies for Fusion Codes. In: Yiannis Cotronis, Marco Danuletto, George Papadopoullos (Eds.), Proceedings of the 19th International Euromicro Conference on Parallel, Distributed and Network-Based Computing, 357–364, IEEE Computer Society, 2011.

Abstract:

We have previously documented the on-going work in the EUFORIA project to parallelise and
optimise European fusion simulation codes, see [1]. This involves working with a wide range
of codes to try and address any performance and scaling issues that these codes have.
However, as no two simulation codes are exactly the same, it is very hard to apply exactly
the same approach to optimising a disparate range of codes. Indeed, it can be seen from [1]
that the codes investigate range in terms of performance and ability from well-optimised,
highly parallelised codes, to serial or poorly performing codes.

After analysing, optimising, parallelising, and scaling a range of codes it is, actually, possible
to discern a number of distinct optimisation techniques or approaches/strategies that can be
used to improve the performance or scaling of a parallel simulation code.
This paper outlines the distinct approaches that we have identified, highlighting their benefits
and drawbacks, giving an overview of the type of work that is often attempted for fusion
simulation code optimisation.

BibTeX entry:

@INPROCEEDINGS{inpJaReBoHeWeAsCaSo11a,
  title = {Parallel Optimisation Strategies for Fusion Codes},
  booktitle = {Proceedings of the 19th International Euromicro Conference on Parallel, Distributed and Network-Based Computing},
  author = {Jackson, Adrian and Reid, Fiona and Booth, Stephen and Hein, Joachim and Westerholm, Jan and Aspnäs, Mats and Català, Miquel and Soba, Alejandro},
  editor = {Cotronis, Yiannis and Danuletto, Marco and Papadopoullos, George},
  publisher = {IEEE Computer Society},
  pages = {357–364},
  year = {2011},
  keywords = {fusion, HPC, optimisation, parallelisation},
}

Belongs to TUCS Research Unit(s): High Performance Computing and Communication

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