Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation

Abstract
There has been a large increase in the amount of work on hierarchical low-rank approximation methods, where the interest is shared by multiple communities that previously did not intersect. This objective of this article is two-fold; to provide a thorough review of the recent advancements in this field from both analytical and algebraic perspectives, and to present a comparative benchmark of two highly optimized implementations of contrasting methods for some simple yet representative test cases. The first half of this paper has the form of a survey paper, to achieve the former objective. We categorize the recent advances in this field from the perspective of compute-memory tradeoff, which has not been considered in much detail in this area. Benchmark tests reveal that there is a large difference in the memory consumption and performance between the different methods.

Citation
Yokota R, Ibeid H, Keyes D (2017) Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation. Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing: 267–286. Available: http://dx.doi.org/10.1007/978-3-319-62426-6_17.

Acknowledgements
We thank François-Henry Rouet, Pieter Ghysels, and Xiaoye, S. Li for providing the STRUMPACK interface for our comparisons between FMM and HSS. This work was supported by JSPS KAKENHI Grant-in-Aid for Research Activity Start-up Grant Number 15H06196. This publication was based on work supported in part by Award No KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575.

Publisher
Springer Nature

Journal
Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing

Conference/Event Name
1st InternationalWorkshop on Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing, EPASA 2015

DOI
10.1007/978-3-319-62426-6_17

arXiv
1602.02244

Additional Links
https://link.springer.com/chapter/10.1007%2F978-3-319-62426-6_17

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