Pragmatic optimizations for better scientific utilization of large supercomputers

Authors:
Anshu Dubey;Alan C. Calder;Christopher Daley;Robert T. Fisher;C. Graziani;George C. Jordan;Donald Q. Lamb;Lynn B. Reid;Dean M. Townsley;Klaus Weide
Affiliations:
Flash Center for Computational Science, Astronomy and Astrophysics, Computation Institute, The University of Chicago, Chicago, IL, USA, Argonne National Laboratory, Argonne, IL, USA;Department of Physics and Astronomy, Stony Brook University, USA;Flash Center for Computational Science, Astronomy and Astrophysics, Computation Institute, The University of Chicago, Chicago, IL, USA, Argonne National Laboratory, Argonne, IL, USA;Department of Physics, University of Massachusetts Dartmouth, USA;Flash Center for Computational Science, Astronomy and Astrophysics, Computation Institute, The University of Chicago, Chicago, IL, USA, Argonne National Laboratory, Argonne, IL, USA;Flash Center for Computational Science, Astronomy and Astrophysics, Computation Institute, The University of Chicago, Chicago, IL, USA;Flash Center for Computational Science, Astronomy and Astrophysics, Computation Institute, The University of Chicago, Chicago, IL, USA, Argonne National Laboratory, Argonne, IL, USA;NTEC Environmental Technology, Subiaco WA, Australia, University of Western Australia, Crawley WA, Australia;Department of Physics and Astronomy, The University of Alabama, USA;Flash Center for Computational Science, Astronomy and Astrophysics, Computation Institute, The University of Chicago, Chicago, IL, USA
Venue:
International Journal of High Performance Computing Applications
Year:
2013

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Abstract

Advances in modeling and algorithms, combined with growth in computing resources, have enabled simulations of multiphysics-multiscale phenomena that can greatly enhance our scientific understanding. However, on currently available high-performance computing (HPC) resources, maximizing the scientific outcome of simulations requires many trade-offs. In this paper we describe our experiences in running simulations of the explosion phase of Type Ia supernovae on the largest available platforms. The simulations use FLASH, a modular, adaptive mesh, parallel simulation code with a wide user base. The simulations use multiple physics components: hydrodynamics, gravity, a sub-grid flame model, a three-stage burning model, and a degenerate equation of state. They also use Lagrangian tracer particles, which are then post-processed to determine the nucleosynthetic yields. We describe the simulation planning process, and the algorithmic optimizations and trade-offs that were found to be necessary. Several of the optimizations and trade-offs were made during the course of the simulations as our understanding of the challenges evolved, or when simulations went into previously unexplored physical regimes. We also briefly outline the anticipated challenges of, and our preparations for, the next-generation computing platforms.