ALPINE: A set of performance portable plasma physics particle-in-cell mini-apps for exascale computing

Alpine consists of a set of mini-apps that makes use of exascale computing capabilities to numerically solve some classical problems in plasma physics. It is based on IPPL (Independent Parallel Particle Layer), a framework that is designed around performance portable and dimension independent particles and fields. In this work, IPPL is used to implement a particle-in-cell scheme. The article describes in detail the following mini-apps: weak and strong Landau damping, bump-on-tail and two-stream instabilities, and the dynamics of an electron bunch in a charge-neutral Penning trap. We benchmark the simulations with varying parameters such as grid resolutions (512^3 to 2048^3) and number of simulation particles (10^9 to 10^11). We show strong and weak scaling and analyze the performance of different components on several pre-exascale architectures such as Piz-Daint, Cori, Summit and Perlmutter.

READ FULL TEXT
research
05/06/2020

A Smoothed Particle Hydrodynamics Mini-App for Exascale

The Smoothed Particles Hydrodynamics (SPH) is a particle-based, meshfree...
research
02/25/2021

VPIC 2.0: Next Generation Particle-in-Cell Simulations

VPIC is a general purpose Particle-in-Cell simulation code for modeling ...
research
09/19/2021

Enabling particle applications for exascale computing platforms

The Exascale Computing Project (ECP) is invested in co-design to assure ...
research
04/27/2022

A Task Programming Implementation for the Particle in Cell Code Smilei

An implementation of the electromagnetic Particle in Cell loop in the co...
research
09/21/2018

Towards a Mini-App for Smoothed Particle Hydrodynamics at Exascale

The smoothed particle hydrodynamics (SPH) technique is a purely Lagrangi...
research
04/17/2018

An Efficient SIMD Implementation of Pseudo-Verlet Lists for Neighbour Interactions in Particle-Based Codes

In particle-based simulations, neighbour finding (i.e finding pairs of p...
research
09/09/2018

Nonparametric semisupervised classification for signal detection in high energy physics

Model-independent searches in particle physics aim at completing our kno...

Please sign up or login with your details

Forgot password? Click here to reset