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

Rio Yokota, Huda Ibeid, David E. Keyes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationEigenvalue Problems: Algorithms, Software and Applications in Petascale Computing
PublisherSpringer Nature
Pages267-286
Number of pages20
ISBN (Print)9783319624242
DOIs
StatePublished - Oct 4 2017

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