Petascale block-structured AMR applications without distributed meta-data

Authors:
Brian Van Straalen;Phil Colella;Daniel T. Graves;Noel Keen
Affiliations:
Applied Numerical Algorithms Group, Lawrence Berkeley National Laboratory, Berkeley, CA;Applied Numerical Algorithms Group, Lawrence Berkeley National Laboratory, Berkeley, CA;Applied Numerical Algorithms Group, Lawrence Berkeley National Laboratory, Berkeley, CA;Applied Numerical Algorithms Group, Lawrence Berkeley National Laboratory, Berkeley, CA
Venue:
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
Year:
2011

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Abstract

Adaptive mesh refinement (AMR) applications to solve partial differential equations (PDE) are very challenging to scale efficiently to the petascale regime. We describe optimizations to the Chombo AMR framework that enable it to scale efficiently to petascale on the Cray XT5. We describe an example of a hyperbolic solver (inviscid gas dynamics) and an matrixfree geometric multigrid elliptic solver. Both show good weak scaling to 131K processors without any thread-level or SIMD vector parallelism. This paper describes the algorithms used to compress the Chombo metadata and the optimizations of the Chombo infrastructure that are necessary for this scaling result. That we are able to achieve petascale performance without distribution of the metadata is a significant advance which allows for much simpler and faster AMR codes.