IPAC2017 - List of Authors (Liu, Zh.C.)

Paper	Title	Page
THPAB027	Symplectic Multi-Particle Tracking Using Cuda	3756
SUSPSIK066	use link to see paper's listing under its alternate paper code
	Zh.C. Liu IHEP, Beijing, People's Republic of China J. Qiang LBNL, Berkeley, California, USA
	Funding: Work supported by the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and the Ministry of Science and Technology of China under Grant No.2014CB845501. The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using Graphic Processing Units (GPUs). Using a single GPU, the code achieves a speedup of more than 400 compared with the time on a single CPU core. It also shows good scalability on a GPU cluster at Oak Ridge Leadership Computing Facility. In this paper, we report on the GPU code implement, the performance test on both single-GPU and multi-GPU cluster, and an application of beam dynamics simulation.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2017-THPAB027
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Paper

Title

Page

THPAB027

Symplectic Multi-Particle Tracking Using Cuda

3756

SUSPSIK066

use link to see paper's listing under its alternate paper code

Zh.C. Liu
IHEP, Beijing, People's Republic of China
J. Qiang
LBNL, Berkeley, California, USA

Funding: Work supported by the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and the Ministry of Science and Technology of China under Grant No.2014CB845501.
The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using Graphic Processing Units (GPUs). Using a single GPU, the code achieves a speedup of more than 400 compared with the time on a single CPU core. It also shows good scalability on a GPU cluster at Oak Ridge Leadership Computing Facility. In this paper, we report on the GPU code implement, the performance test on both single-GPU and multi-GPU cluster, and an application of beam dynamics simulation.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2017-THPAB027

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)