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Tiwari, D., Gupta, S., Gallarno, G., Rogers, J. and Maxwell, D. (2015) Reliability Lessons Learned from GPU Experience with the Titan Supercomputer at Oak Ridge Leadership Computing Facility. In: Proceedings of the International Conference for High-Performance Computing, Networking, Storage and Analysis, ACM, New York, 38.
https://doi.org/10.1145/2807591.2807666

has been cited by the following article:

• TITLE: Implementation of a Particle Accelerator Beam Dynamics Code on Multi-Node GPUs

  AUTHORS: Zhicong Liu, Ji Qiang

  KEYWORDS: Particle Accelerator, Particle-In-Cell, GPU, Parallel Beam Dynamics Simulation

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.12 No.9, September 4, 2019

  ABSTRACT: Particle accelerators play an important role in a wide range of scientific discoveries and industrial applications. The self-consistent multi-particle simulation based on the particle-in-cell (PIC) method has been used to study charged particle beam dynamics inside those accelerators. However, the PIC simulation is time-consuming and needs to use modern parallel computers for high-resolution applications. In this paper, we implemented a parallel beam dynamics PIC code on multi-node hybrid architecture computers with multiple Graphics Processing Units (GPUs). We used two methods to parallelize the PIC code on multiple GPUs and observed that the replication method is a better choice for moderate problem size and current computer hardware while the domain decomposition method might be a better choice for large problem size and more advanced computer hardware that allows direct communications among multiple GPUs. Using the multi-node hybrid architectures at Oak Ridge Leadership Computing Facility (OLCF), the optimized GPU PIC code achieves a reasonable parallel performance and scales up to 64 GPUs with 16 million particles.