A universal concurrent algorithm for plasma particle-in-cell simulation codes
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This paper proposes a new method for Particle-in-Cell (PIC) simulations which aims at achieving both good load balancing and scalability so as to be efficiently executed on distributed memory systems. This method, named OhHelp, simply and equally partitions the space domain where charged particles reside and assigns each partitioned subdomain to each computation node for scalable simulation with respect to the size of the domain. Load balancing and thus the scalability in terms of the number of particles are accomplished by making each node help another heavily loaded node which deputes a part of particles in its subdomain and replicated subdomain field data to its helpers. The OhHelp load balancer monitors particle movements through subdomain boundaries to check if the helpand-helpers configuration keeps good load balancing and, when unacceptable imbalance is found, dynamically reconfigures it to regain perfect balancing. The efficiency and scalability of OhHelp are confirmed through our experiment with a production-level full-3D plasma simulator and with uniform and non-uniform particle distributions. As a result, we found 256-core parallel simulations, including an extremely imbalanced setting to pack all the particles in a small region, exert 159-190 speedup compared to sequential execution.