A Fast Free Memory Method for an Efficient Computation of Convolution Kernels

A Fast Free Memory Method for an Efficient Computation of Convolution Kernels

Year:    2023

Author:    Matthieu Aussal, Marc Bakry

Journal of Computational Mathematics, Vol. 41 (2023), Iss. 6 : pp. 1093–1116

Abstract

We introduce the Fast Free Memory method (FFM), a new implementation of the Fast Multipole Method (FMM) for the evaluation of convolution products. The FFM aims at being easier to implement while maintaining a high level of performance, capable of handling industrially-sized problems. The FFM avoids the implementation of a recursive tree and is a kernel independent algorithm. We give the algorithm and the relevant complexity estimates. The quasi-linear complexity enables the evaluation of convolution products with up to one billion entries. We illustrate numerically the capacities of the FFM by solving Boundary Integral Equations problems featuring dozen of millions of unknowns. Our implementation is made freely available under the GPL 3.0 license within the Gypsilab framework.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.2202-m2021-0324

Journal of Computational Mathematics, Vol. 41 (2023), Iss. 6 : pp. 1093–1116

Published online:    2023-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    24

Keywords:    Convolution product Fast multipole method Boundary integral equations Open-source.

Author Details

Matthieu Aussal

Marc Bakry