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Massively parallelized interpolated factored Green function method
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DOI：10.1016/j.jcp.2022.111837.png)
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Abstract
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This paper presents the first parallel implementation of the novel Interpolated Factored Green Function (IFGF) method introduced recently for the accelerated evaluation of discrete integral operators arising in wave scattering and other areas (Bauinger and Bruno, Jour. Computat. Phys., 2021). On the basis of the hierarchical IFGF interpolation strategy, the proposed (hybrid MPI-OpenMP) parallel implementation results in efficient data communication, and it scales up to large numbers of cores-without any hard limitations on the number of cores efficiently employed. Moreover, on any given number of cores, the proposed parallel approach preserves the O(N log N) computing cost inherent in the sequential version of the IFGF algorithm. Unlike other approaches, the IFGF method does not utilize the Fast Fourier Transform (FFT), and it is thus better suited for efficient parallelization in distributed-memory computer systems. In particular, the IFGF method relies on a peer-to-peer strategy wherein, at every level, field propagation is directly enacted via exchanges between peer polynomials of constant degree, without data accumulation in large-scale telephone-central mathematical constructs such as those used in the Fast Multipole Method (FMM) and pure FFT-based approaches. A variety of numerical results presented in this paper illustrate the character of the proposed parallel algorithm, in particular demonstrating scaling from 1 to all 1,680 cores available in the High Performance Computing cluster used, and for problems of up to 4,096 wavelengths in acoustic size.(c) 2022 Elsevier Inc. All rights reserved.
Keywords:
Parallelization
Green function
Integral equations
Distributed memory acceleration
OpenMP
MPI
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