Load balancing for particle-in-cell plasma simulation on multicore systems
Paper i proceeding, 2018

Particle-in-cell plasma simulation is an important area of computational physics. The particle-in-cell method naturally allows parallel processing on distributed and shared memory. In this paper we address the problem of load balancing on multicore systems. While being well-studied for many traditional applications of the method, it is a relevant problem for the emerging area of particle-in-cell simulations with account for effects of quantum electrodynamics. Such simulations typically produce highly non-uniform, and sometimes volatile, particle distributions, which could require custom load balancing schemes. In this paper we present a computational evaluation of several standard and custom load balancing schemes for the particle-in-cell method on a high-end system with 96 cores on shared memory. We use a test problem with static non-uniform particle distribution and a real problem with account for quantum electrodynamics effects, which produce dynamically changing highly non-uniform distributions of particles and workload. For these problems the custom schemes result in increase of scaling efficiency by up to 20% compared to the standard OpenMP schemes.

Particle-in-cell

OpenMP

Load balancing

Plasma simulation

Quantum electrodynamics

Parallel processing

Författare

Anton Larin

Lobachevsky University

Russian Academy of Sciences

S. Bastrakov

Lobachevsky University

A. Bashinov

Russian Academy of Sciences

E. Efimenko

Russian Academy of Sciences

I. Surmin

Lobachevsky University

Arkady Gonoskov

Lobachevsky University

Russian Academy of Sciences

Chalmers, Fysik, Teoretisk fysik

I. Meyerov

Lobachevsky University

Lecture Notes in Computer Science

0302-9743 (ISSN)

Vol. 10777 145-155

12th International Conference on Parallel Processing and Applied Mathematics, PPAM 2017
Czestochowa, Poland,

Ämneskategorier

Datorteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1007/978-3-319-78024-5_14

Mer information

Senast uppdaterat

2018-05-29