A Massively Parallel Performance Portable Free-space Spectral Poisson Solver
<|reference_start|>A Massively Parallel Performance Portable Free-space Spectral Poisson Solver: Vico et al. (2016) suggest a fast algorithm for computing volume potentials, beneficial to fields with problems requiring the solution of Poisson's equation with free-space boundary conditions, such as the beam and plasma physics communities. Currently, the standard method for solving the free-space Poisson equation is the algorithm of Hockney and Eastwood (1988), which is second order in convergence at best. The algorithm proposed by Vico et al. converges spectrally for sufficiently smooth functions i.e. faster than any fixed order in the number of grid points. In this paper, we implement a performance portable version of the traditional Hockney-Eastwood and the novel Vico-Greengard Poisson solver as part of the IPPL (Independent Parallel Particle Layer) library. For sufficiently smooth source functions, the Vico-Greengard algorithm achieves higher accuracy than the Hockney-Eastwood method with the same grid size, reducing the computational demands of high resolution simulations since one could use coarser grids to achieve them. More concretely, to get a relative error of $10^{-4}$ between the numerical and analytical solution, one requires only $16^3$ grid points in the former, but $128^3$ in the latter, more than a 99% memory footprint reduction. Additionally, we propose an algorithmic improvement to the Vico-Greengard method which further reduces its memory footprint. This is particularly important for GPUs which have limited memory resources, and should be taken into account when selecting numerical algorithms for performance portable codes. Finally, we showcase performance through GPU and CPU scaling studies on the Perlmutter (NERSC) supercomputer, with efficiencies staying above 50% in the strong scaling case.<|reference_end|>
@article{mayani2024a,
    title={A Massively Parallel Performance Portable Free-space Spectral Poisson
  Solver},
    author={Sonali Mayani, Veronica Montanaro, Antoine Cerfon, Matthias Frey,
  Sriramkrishnan Muralikrishnan, Andreas Adelmann},
    journal={arXiv preprint arXiv:2405.02603},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02603},
    primaryClass={physics.comp-ph cs.DC}
}