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DTSTAMP:20241120T082410Z
LOCATION:HG F 30 Audi Max
DTSTART;TZID=Europe/Stockholm:20240604T100600
DTEND;TZID=Europe/Stockholm:20240604T100700
UID:submissions.pasc-conference.org_PASC24_sess158_posC102@linklings.com
SUMMARY:ACMP05 - Efficient, Portable, Massively Parallel Free-Space Solver
 s for the Poisson Equation
DESCRIPTION:Poster\n\nSonali Mayani (Paul Scherrer Institute, ETH Zurich);
  Antoine Cerfon (New York University, Type One Energy); Matthias Frey (Uni
 versity of St Andrews); Veronica Montanaro (ETH Zurich); Sriramkrishnan Mu
 ralikrishnan (Forschungszentrum Jülich); and Andreas Adelmann (Paul Scherr
 er Institute, ETH Zurich)\n\nVico et al. (2016) suggest a fast algorithm f
 or computing volume potentials which is of benefit to the beam and plasma 
 physics communities, as they require the solution of Poisson’s equation wi
 th free-space boundary conditions. The standard method to solve the free-s
 pace Poisson equation is to use the algorithm presented by Hockney and Eas
 twood (1988), which is second order in convergence at best. The algorithm 
 proposed by Vico et al., which we refer to as Vico-Greengard, converges sp
 ectrally, i.e. faster than any fixed order of the number of grid points, f
 or smooth enough functions. We implement a performance portable Poisson so
 lver in the framework of the IPPL (Independent Parallel Particle Layer) li
 brary based on these two methods: the traditional Hockney-Eastwood, and th
 e novel Vico-Greengard. Furthermore, we suggest an improvement to the Vico
 -Greengard algorithm which reduces its memory footprint. We show that for 
 sufficiently smooth distribution functions, the Vico-Greengard algorithm c
 ould be a good candidate for reducing memory usage, since better accuracy 
 can be obtained with a coarser grid. This is especially significant for GP
 Us, which present memory constraints. Finally, we showcase performance thr
 ough scaling studies on the Perlmutter (NERSC) supercomputer, with efficie
 ncies staying above 50% in the strong scaling case.\n\nSession Chair: Iva 
 Kavcic (Met Office)
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