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Numerical tests indicate that FMM attains accuracy comparable to that of MG for smooth potentials and is particularly well-suited for problems with isolated boundary conditions, since it avoids the approximate Dirichlet boundary conditions required by MG schemes.
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Abstract We present a scalable Poisson solver with $\mathcal {O}(N)$ complexity based on the fast multipole method (FMM) implemented in RAMSES. Our FMM constructs a hierarchy of FMM grids on top of the pre-existing Cartesian grid which is used to compute the force for hydrodynamics or particle–mesh simulations. In contrast to the $\mathcal {O}(N)$ multigrid solver (MG) — an iterative method that requires multiple V-cycles through a multi-resolution hierarchy of Cartesian grids — the FMM algorithm performs just one upward pass through the same hierarchy, during which multipole expansions are accumulated and shifted, followed by a single downward pass, in which local expansions are propagated. Numerical tests indicate that FMM attains accuracy comparable to that of MG for smooth potentials and is particularly well-suited for problems with isolated boundary conditions, since it avoids the approximate Dirichlet boundary conditions required by MG schemes. Although in theory FMM requires around 30 times more floating-point operations than MG, its higher arithmetic intensity leads to comparable performance and better scalability relative to MG.
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@article{Lee2026Scalable,
title = {A Scalable Fast Multipole Method Poisson Solver for the RAMSES code: I. Unigrid Algorithm},
author = {Jun-Young Lee and Romain Teyssier},
journal = {Monthly Notices of the Royal Astronomical Society},
year = {2026},
doi = {10.1093/mnras/stag1241},
url = {https://doi.org/10.1093/mnras/stag1241}
}
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