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Parallelizing Fast Multipole Method for Large-Scale Electromagnetic Problems Using GPU Clusters
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DOI：10.1109/LAWP.2013.2271743.png)
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Abstract
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This letter investigates the solution of large-scale electromagnetic problems by using the single-level Fast Multipole Method (FMM). Problems of large scale require high computational capability that cannot be accommodated using conventional computing systems. We investigate a parallel implementation of FMM on a 13-node graphics processing unit (GPU) cluster that populates Nvidia Tesla M2090 GPUs. The implementation details and the performance achievements in terms of accuracy, speedup, and scalability are discussed. The experimental results demonstrate that our FMM implementation on GPUs is much faster than (up to 700x) that of the CPU implementation. Moreover, the scalability of the GPU implementation is very close to the theoretical linear expectations.
Keywords:
Fast Multipole Method (FMM)
graphics processing unit (GPU)
high-performance clusters
iterative solvers
method of moments (MoM)
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