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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2017-150
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 13 Jul 2017

Development and technical paper | 13 Jul 2017

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Geoscientific Model Development (GMD).

Portable Multi- and Many-Core Performance for Finite Difference Codes; Application to the Free-Surface Component of NEMO

Andrew Porter1, Jeremy Appleyard2, Mike Ashworth1, Rupert Ford1, Jason Holt3, Hedong Liu3, and Graham Riley4 Andrew Porter et al.
  • 1Science and Technology Facilities Council, Daresbury Laboratory, UK
  • 2NVIDIA Corporation
  • 3National Oceanography Centre, Liverpool, UK
  • 4University of Manchester, Manchester, UK

Abstract. We present an approach which we call PSyKAl that is designed to achieve portable performance for parallel, finite-difference Ocean models. In PSyKAl the code related to the underlying science is formally separated from code related to parallelisation and single-core optimisations. This separation of concerns allows scientists to code their science independently of the underlying hardware architecture and for optimisation specialists to be able to tailor the code for a particular machine independently of the science code. We have taken the free-surface part of the NEMO ocean model and created a new, shallow-water model named NEMOLite2D. In doing this we have a code which is of a manageable size and yet which incorporates elements of full ocean models (input/output, boundary conditions, etc.). We have then manually constructed a PSyKAl version of this code and investigated the transformations that must be applied to the middle/PSy layer in order to achieve good performance, both serial and parallel. We have produced versions of the PSy layer parallelised with both OpenMP and OpenACC; in both cases we were able to leave the natural-science parts of the code unchanged while achieving good performance on both multi-core CPUs and GPUs. In quantifying whether or not the obtained performance is `good' we also consider the limitations of the basic roofline model and improve on it by generating kernel-specific CPU ceilings.

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Short summary
Developing computer models in the earth-system domain is a complex and expensive process that can have a duration measured in years. The supercomputers required to run these models are however evolving fast with a proliferation of technologies and associated programming models. As a result there is a need that models be 'performance portable' between different supercomputers. This paper investigates a way of doing this through a separation of the concerns of performance and natural science.
Developing computer models in the earth-system domain is a complex and expensive process that...
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