Predictive analysis of a hydrodynamics application on large-scale CMP clusters

Authors:
J. A. Davis;G. R. Mudalige;S. D. Hammond;J. A. Herdman;I. Miller;S. A. Jarvis
Affiliations:
Performance Computing and Visualisation, Department of Computer Science, University of Warwick, Coventry, UK CV4 7AL;Oxford eResearch Centre, University of Oxford, Oxford, UK OX1 3QG;Performance Computing and Visualisation, Department of Computer Science, University of Warwick, Coventry, UK CV4 7AL;Atomic Weapons Establishment, Aldermaston, Reading, UK RG7 4PR;Atomic Weapons Establishment, Aldermaston, Reading, UK RG7 4PR;Performance Computing and Visualisation, Department of Computer Science, University of Warwick, Coventry, UK CV4 7AL
Venue:
Computer Science - Research and Development
Year:
2011

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Abstract

We present the development of a predictive performance model for the high-performance computing code Hydra, a hydrodynamics benchmark developed and maintained by the United Kingdom Atomic Weapons Establishment (AWE). The developed model elucidates the parallel computation of Hydra, with which it is possible to predict its run-time and scaling performance on varying large-scale chip multiprocessor (CMP) clusters. A key feature of the model is its granularity; with the model we are able to separate the contributing costs, including computation, point-to-point communications, collectives, message buffering and message synchronisation. The predictions are validated on two contrasting large-scale HPC systems, an AMD Opteron/InfiniBand cluster and an IBM BlueGene/P, both of which are located at the Lawrence Livermore National Laboratory (LLNL) in the US. We validate the model on up to 2,048 cores, where it achieves a 85% accuracy in weak-scaling studies. We also demonstrate use of the model in exposing the increasing costs of collectives for this application, and also the influence of node density on network accesses, therefore highlighting the impact of machine choice when running this hydrodynamics application at scale.