Task-Based FMM for Multicore Architectures

Emmanuel Agullo 1, 2 Bérenger Bramas 1, 2 Olivier Coulaud 2, 1 Eric Darve 3, 4 Matthias Messner 1, 2 Toru Takahashi 5
1 HiePACS - High-End Parallel Algorithms for Challenging Numerical Simulations
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
2 LaBRI - Laboratoire Bordelais de Recherche en Informatique
3 Mechanical Engineering Department [Stanford]
4 iCME - Institute for Computational and Mathematical Engineering
5 Department of Mechanical Science and Engineering
Abstract : Fast Multipole Methods (FMM) are a fundamental operation for the simulation of many physical problems. The high performance design of such methods usually requires to carefully tune the algorithm for both the targeted physics and hardware. In this paper, we propose a new approach that achieves high performance across architectures. Our method consists of expressing the FMM algorithm as a task flow and employing a state- of-the-art runtime system, StarPU, to process the tasks on the different computing units. We carefully design the task flow, the mathematical operators, their implementations as well as scheduling schemes. Potentials and forces on 200 million particles are computed in 42.3 seconds on a homogeneous 160 cores SGI Altix UV 100 and good scalability is shown.
Keywords : fast multipole method multicore architectures shared memory paradigm runtime system pipeline
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Article dans une revue
SIAM Journal on Scientific Computing, Society for Industrial and Applied Mathematics, 2014, 36 (1), pp.66-93. 〈10.1137/130915662〉
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Emmanuel Agullo, Bérenger Bramas, Olivier Coulaud, Eric Darve, Matthias Messner, et al.. Task-Based FMM for Multicore Architectures. SIAM Journal on Scientific Computing, Society for Industrial and Applied Mathematics, 2014, 36 (1), pp.66-93. 〈10.1137/130915662〉. 〈hal-00911856〉

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