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Title: Symplectic multi-particle tracking on GPUs

Abstract

A symplectic multi-particle tracking model is implemented on the Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) language. The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation, which is important for beam property evaluation in particle accelerators. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using GPUs. In this paper, we optimized the implementation of the symplectic tracking model on both single GPU and multiple GPUs. Using a single GPU processor, the code achieves a factor of 2-10 speedup for a range of problem sizes compared with the time on a single state-of-the-art Central Processing Unit (CPU) node with similar power consumption and semiconductor technology. It also shows good scalability on a multi-GPU cluster at Oak Ridge Leadership Computing Facility. In an application to beam dynamics simulation, the GPU implementation helps save more than a factor of two total computing time in comparison to the CPU implementation.

Authors:
ORCiD logo [1];  [2]
  1. Chinese Academy of Sciences (CAS), Beijing (China). Inst. of High Energy Physics (IHEP), Key Lab. of Particle Acceleration Physics and Technology; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of Chinese Academy of Sciences (CAS), Beijing (China)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Lawrence Berkeley National Laboratory, Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1526520
Alternate Identifier(s):
OSTI ID: 1548755
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Computer Physics Communications
Additional Journal Information:
Journal Volume: 226; Journal Issue: C; Journal ID: ISSN 0010-4655
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 43 PARTICLE ACCELERATORS; Particle accelerator; Symplectic; Multi-particle tracking; GPU

Citation Formats

Liu, Zhicong, and Qiang, Ji. Symplectic multi-particle tracking on GPUs. United States: N. p., 2018. Web. doi:10.1016/j.cpc.2018.02.001.
Liu, Zhicong, & Qiang, Ji. Symplectic multi-particle tracking on GPUs. United States. doi:10.1016/j.cpc.2018.02.001.
Liu, Zhicong, and Qiang, Ji. Tue . "Symplectic multi-particle tracking on GPUs". United States. doi:10.1016/j.cpc.2018.02.001. https://www.osti.gov/servlets/purl/1526520.
@article{osti_1526520,
title = {Symplectic multi-particle tracking on GPUs},
author = {Liu, Zhicong and Qiang, Ji},
abstractNote = {A symplectic multi-particle tracking model is implemented on the Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) language. The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation, which is important for beam property evaluation in particle accelerators. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using GPUs. In this paper, we optimized the implementation of the symplectic tracking model on both single GPU and multiple GPUs. Using a single GPU processor, the code achieves a factor of 2-10 speedup for a range of problem sizes compared with the time on a single state-of-the-art Central Processing Unit (CPU) node with similar power consumption and semiconductor technology. It also shows good scalability on a multi-GPU cluster at Oak Ridge Leadership Computing Facility. In an application to beam dynamics simulation, the GPU implementation helps save more than a factor of two total computing time in comparison to the CPU implementation.},
doi = {10.1016/j.cpc.2018.02.001},
journal = {Computer Physics Communications},
number = C,
volume = 226,
place = {United States},
year = {2018},
month = {5}
}

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