Enabling scalable parallel implementations of structured adaptive mesh refinement applications

Authors:
Sumir Chandra;Xiaolin Li;Taher Saif;Manish Parashar
Affiliations:
The Applied Software Systems Laboratory, Department of Electrical and Computer Engineering, Organization to Rutgers, The State University of New Jersey, Piscataway, USA 08854;Aff1 Aff2;The Applied Software Systems Laboratory, Department of Electrical and Computer Engineering, Organization to Rutgers, The State University of New Jersey, Piscataway, USA 08854;The Applied Software Systems Laboratory, Department of Electrical and Computer Engineering, Organization to Rutgers, The State University of New Jersey, Piscataway, USA 08854
Venue:
The Journal of Supercomputing
Year:
2007

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Abstract

Parallel implementations of dynamic structured adaptive mesh refinement (SAMR) methods lead to significant runtime management challenges that can limit their scalability on large systems. This paper presents a runtime engine that addresses the scalability of SAMR applications with localized refinements and high SAMR efficiencies on large numbers of processors (upto 1024 processors). The SAMR runtime engine augments hierarchical partitioning with bin-packing based load-balancing to manage the space-time heterogeneity of the SAMR grid hierarchy, and includes a communication substrate that optimizes the use of MPI non-blocking communication primitives. An experimental evaluation on the IBM SP2 supercomputer using the 3-D Richtmyer-Meshkov compressible turbulence kernel demonstrates the effectiveness of the runtime engine in improving SAMR scalability.