Scalable adaptive mantle convection simulation on petascale supercomputers

  • Authors:

  • Carsten Burstedde;Omar Ghattas;Michael Gurnis;Georg Stadler;Eh Tan;Tiankai Tu;Lucas C. Wilcox;Shijie Zhong

  • Affiliations:

  • The University of Texas at Austin, Austin, Texas;The University of Texas at Austin, Austin, Texas;California Institute of Technology, Pasadena, California;The University of Texas at Austin, Austin, Texas;California Institute of Technology, Pasadena, California;The University of Texas at Austin, Austin, Texas;The University of Texas at Austin, Austin, Texas;University of Colorado, Boulder, Colorado

  • Venue:
  • Proceedings of the 2008 ACM/IEEE conference on Supercomputing
  • Year:
  • 2008

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Abstract

Mantle convection is the principal control on the thermal and geological evolution of the Earth. Mantle convection modeling involves solution of the mass, momentum, and energy equations for a viscous, creeping, incompressible non-Newtonian fluid at high Rayleigh and Peclet numbers. Our goal is to conduct global mantle convection simulations that can resolve faulted plate boundaries, down to 1 km scales. However, uniform resolution at these scales would result in meshes with a trillion elements, which would elude even sustained petaflops supercomputers. Thus parallel adaptive mesh refinement and coarsening (AMR) is essential. We present RHEA, a new generation mantle convection code designed to scale to hundreds of thousands of cores. RHEA is built on ALPS, a parallel octree-based adaptive mesh finite element library that provides new distributed data structures and parallel algorithms for dynamic coarsening, refinement, rebalancing, and repartitioning of the mesh. ALPS currently supports low order continuous Lagrange elements, and arbitrary order discontinuous Galerkin spectral elements, on octree meshes. A forest-of-octrees implementation permits nearly arbitrary geometries to be accommodated. Using TACC's 579 teraflops Ranger supercomputer, we demonstrate excellent weak and strong scalability of parallel AMR on up to 62,464 cores for problems with up to 12.4 billion elements. With RHEA'S adaptive capabilities, we have been able to reduce the number of elements by over three orders of magnitude, thus enabling us to simulate large-scale mantle convection with finest local resolution of 1.5 km.