Keyword: GPU
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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.  
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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.  
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