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TASK-BASED FMM FOR MULTICORE ARCHITECTURES
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DOI：10.1137/130915662.png)
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Abstract
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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 the 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, and scheduling schemes. Potentials and forces on 200 million particles are computed in 42.3 seconds on a homogeneous 160-core SGI Altix UV 100 and good scalability is shown.
Keywords:
fast multipole methods
multicore architectures
shared memory paradigm
runtime system
pipeline
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