Algorithm 967

swMATH ID: 23693
Software Authors: Malhotra, Dhairya; Biros, George
Description: Algorithm 967: a distributed-memory fast multipole method for volume potentials. The solution of a constant-coefficient elliptic partial differential equation (PDE) can be computed using an integral transform: A convolution with the fundamental solution of the PDE, also known as a volume potential. We present a fast multipole method (FMM) for computing volume potentials and use them to construct spatially adaptive solvers for the Poisson, Stokes, and low-frequency Helmholtz problems. Conventional \(N\)-body methods apply to discrete particle interactions. With volume potentials, one replaces the sums with volume integrals. Particle \(N\)-body methods can be used to accelerate such integrals. but it is more efficient to develop a special FMM. In this article, we discuss the efficient implementation of such an FMM. We use high-order piecewise Chebyshev polynomials and an octree data structure to represent the input and output fields and enable spectrally accurate approximation of the near-field and the Kernel Independent FMM (KIFMM) for the far-field approximation. For distributed-memory parallelism, we use space-filling curves, locally essential trees, and a hypercube-like communication scheme developed previously in our group. We present new near and far interaction traversals that optimize cache usage. Also, unlike particle \(N\)-body codes, we need a 2:1 balanced tree to allow for precomputations. We present a fast scheme for 2:1 balancing. Finally, we use vectorization, including the AVX instruction set on the Intel Sandy Bridge architecture to get better than 50
Homepage: https://dl.acm.org/citation.cfm?doid=2988256.2898349
Keywords: \(N\)-body problems; potential theory; elliptic partial differential equation; fundamental solution; fast multipole method; Poisson; Stokes; Helmholtz; discrete particle interactions; near-field; far-field; parallelism
Related Software: ScalFMM; pvfmm; TABI; GitHub; exafmm; ASKIT; Chebfun; FMM3D; HOT; ViennaCL; Trilinos; PETSc; HykSort; GADGET; p4est; PPM
Cited in: 6 Publications

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