IPAC2018 - List of Keywords (GPU)

Paper	Title	Other Keywords	Page
THPAK043	Performance Optimization of a Beam Dynamics PIC Code On Hybrid Computer Architectures	simulation, kicker, plasma, HOM	3309
	Zh.C. Liu IHEP, Beijing, People's Republic of China J. Qiang LBNL, Berkeley, California, USA
	The self-consistent multi-particle tracking based on particle-in-cell method (PIC) has been widely used in particle accelerator beam dynamics study. However, the PIC simulation is time-consuming and needs to use modern parallel computers for high resolution applications. In this paper, we implemented and optimized a parallel beam dynamics PIC code on two types of hybrid parallel computer architectures: one is the GPU and GPU cluster, while the other is the "Knight Landing" CPU cluster.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THPAK043
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THPAK088	Matrix Representation of Lie Transform in TensorFlow	network, simulation, storage-ring, linear-dynamics	3438
	A.N. Ivanov, S.N. Andrianov, N.V. Kulabukhova, A.A. Sholokhova St. Petersburg State University, St. Petersburg, Russia E. Krushinevskii, E. Sboeva Saint Petersburg State University, Saint Petersburg, Russia
	In the article, we propose an implementation of the matrix representation of Lie transform using TensorFlow as a computational engine. TensorFlow allows easy description of deep neural networks and provides automatic code execution on both single CPU/GPU and cluster architectures. In this research, we demonstrate the connection of the matrix Lie transform with polynomial neural networks. The architecture of the neural network is described and realized in code. In terms of beam dynamics, the proposed technique provides a tool for both simulation and analysis of experimental results using modern machine learning techniques. As a simulation technique one operates with a nonlinear map up to the necessary order of nonlinearity. On the other hand, one can utilize TensorFlow engine to run map optimization and system identification problems.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THPAK088
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Paper

Title

Other Keywords

Page

THPAK043

Performance Optimization of a Beam Dynamics PIC Code On Hybrid Computer Architectures

simulation, kicker, plasma, HOM

3309

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

The self-consistent multi-particle tracking based on particle-in-cell method (PIC) has been widely used in particle accelerator beam dynamics study. However, the PIC simulation is time-consuming and needs to use modern parallel computers for high resolution applications. In this paper, we implemented and optimized a parallel beam dynamics PIC code on two types of hybrid parallel computer architectures: one is the GPU and GPU cluster, while the other is the "Knight Landing" CPU cluster.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THPAK043

Export •

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

THPAK088

Matrix Representation of Lie Transform in TensorFlow

network, simulation, storage-ring, linear-dynamics

3438

A.N. Ivanov, S.N. Andrianov, N.V. Kulabukhova, A.A. Sholokhova
St. Petersburg State University, St. Petersburg, Russia
E. Krushinevskii, E. Sboeva
Saint Petersburg State University, Saint Petersburg, Russia

In the article, we propose an implementation of the matrix representation of Lie transform using TensorFlow as a computational engine. TensorFlow allows easy description of deep neural networks and provides automatic code execution on both single CPU/GPU and cluster architectures. In this research, we demonstrate the connection of the matrix Lie transform with polynomial neural networks. The architecture of the neural network is described and realized in code. In terms of beam dynamics, the proposed technique provides a tool for both simulation and analysis of experimental results using modern machine learning techniques. As a simulation technique one operates with a nonlinear map up to the necessary order of nonlinearity. On the other hand, one can utilize TensorFlow engine to run map optimization and system identification problems.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THPAK088

Export •

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