Affiliations: Scientific Computation Research Center, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, USA. E-mails: {mubarm, seols, luq3, shephard}@rpi.edu
Note: [] Corresponding author: Ms. Misbah Mubarak, Scientific Computation Research Center (SCOREC), Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, USA. Tel.: +1 518 951 0857; E-mail: [email protected]
Abstract: Critical to the scalability of parallel adaptive simulations are parallel control functions including load balancing, reduced inter-process communication and optimal data decomposition. In distributed meshes, many mesh-based applications frequently access neighborhood information for computational purposes which must be transmitted efficiently to avoid parallel performance degradation when the neighbors are on different processors. This article presents a parallel algorithm of creating and deleting data copies, referred to as ghost copies, which localize neighborhood data for computation purposes while minimizing inter-process communication. The key characteristics of the algorithm are: (1) It can create ghost copies of any permissible topological order in a 1D, 2D or 3D mesh based on selected adjacencies. (2) It exploits neighborhood communication patterns during the ghost creation process thus eliminating all-to-all communication. (3) For applications that need neighbors of neighbors, the algorithm can create n number of ghost layers up to a point where the whole partitioned mesh can be ghosted. Strong and weak scaling results are presented for the IBM BG/P and Cray XE6 architectures up to a core count of 32,768 processors. The algorithm also leads to scalable results when used in a parallel super-convergent patch recovery error estimator, an application that frequently accesses neighborhood data to carry out computation.
