Optimizing the adaptive fast multipole method for fractal sets

TL;DR

This paper analyzes and optimizes the adaptive fast multipole method (FMM) for fractal point distributions, introducing a new complexity analysis, parameter optimization, and a subdividing double-threshold method to enhance performance.

Contribution

It provides a comprehensive framework for adaptive FMM on fractal sets, demonstrating ${ m O}(N)$ complexity and introducing new parameter tuning techniques.

Findings

Adaptive FMM achieves ${ m O}(N)$ complexity for fractal distributions.

New subdividing double-threshold method improves performance.

Optimal parameters depend on fractal dimension of point sets.

Abstract

We have performed a detailed analysis of the fast multipole method (FMM) in the adaptive case, in which the depth of the FMM tree is non-uniform. Previous works in this area have focused mostly on special types of adaptive distributions, for example when points accumulate on a 2D manifold or accumulate around a few points in space. Instead, we considered a more general situation in which fractal sets, e.g., Cantor sets and generalizations, are used to create adaptive sets of points. Such sets are characterized by their dimension, a number between 0 and 3. We introduced a mathematical framework to define a converging sequence of octrees, and based on that, demonstrated how to increase $N \to \infty$. A new complexity analysis for the adaptive FMM is introduced. It is shown that the ${\cal{O}}(N)$ complexity is achievable for any distribution of particles, when a modified adaptive FMM is exploited. We analyzed how the FMM performs for fractal point distributions, and how optimal parameters can be picked, e.g., the criterion used to stop the subdivision of an FMM cell. A new subdividing double-threshold method is introduced, and better performance demonstrated. Parameters in the FMM are modeled as a function of particle distribution dimension, and the optimal values are obtained. A three dimensional kernel independent black box adaptive FMM is implemented and used for all calculations.