Massively parallel frequency domain electromagnetic simulation codes

Langston, William L.; Kotulski, Joseph D.; Coats, R. S.; Jorgenson, Roy E.; Blake, S. Adam; Campione, Salvatore; Pung, Aaron J.; Zinser, Brian

doi:10.23919/ropaces.2018.8364305

Cited by 2 publications

(1 citation statement)

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“…This production Fortran code has been used effectively for a large class of problems and on a variety of compute platforms -its utility has been extended by the ADELUS solver. The next generation version of EIGER, GEMMA [31], is currently being developed to use the Kokkos library to increase performance in the filling of the matrix as well.…”

Section: Application: Methods Of Moments For Linear Electromagneticsmentioning

confidence: 99%

ADELUS: A Performance-Portable Dense LU Solver for Distributed-Memory Hardware-Accelerated Systems

Dang

Kotulski

Rajamanickam

2021

Accelerator Programming Using Directives

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Solving dense systems of linear equations is essential in applications encountered in physics, mathematics, and engineering. This paper describes our current efforts toward the development of the ADELUS package for current and next generation distributed, accelerator-based, high-performance computing platforms. The package solves dense linear systems using partial pivoting LU factorization on distributed-memory systems with CPUs/GPUs. The matrix is blockmapped onto distributed memory on CPUs/GPUs and is solved as if it was torus-wrapped for an optimal balance of computation and communication. A permutation operation is performed to restore the results so the torus-wrap distribution is transparent to the user. This package targets performance portability by leveraging the abstractions provided in the Kokkos and Kokkos Kernels libraries. Comparison of the performance gains versus the state-of-the-art SLATE and DPLASMA GESV functionalities on the Summit supercomputer are provided. Preliminary performance results from large-scale electromagnetic simulations using ADELUS are also presented. The solver achieves 7.7 Petaflops on 7600 GPUs of the Sierra supercomputer translating to 16.9% efficiency.

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Section: Application: Methods Of Moments For Linear Electromagneticsmentioning

confidence: 99%