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Enabling Large Scale Simulations for Particle Accelerators

delete2022-12-01
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OA
AI
K
Konstantinos Iliakis *
S
Sotirios Xydis
D
Dimitrios Soudris
DOI:10.1109/TPDS.2022.3192707delete
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摘要

摘要

En 中文
International high-energy particle physics research centers, like CERN and Fermilab, require excessive studies and simulations to plan for the upcoming upgrades of the world's largest particle accelerators, and the design of future machines given the technological challenges and tight budgetary constraints. The Beam Longitudinal Dynamics (BLonD) simulator suite incorporates the most detailed and complex physics phenomena in the field of longitudinal beam dynamics, required for providing extremely accurate predictions. Modern challenges in beam dynamics dictate for longer, larger and numerous simulation studies to draw meaningful conclusions that will drive the baseline choices for the daily operation of current machines and the design choices of future projects. These studies are extremely time consuming, and would be impractical to perform without a High-Performance Computing oriented simulator framework. In this article, at first, we design and evaluate a highly-optimized distributed version of BLonD. We combine approximate computing techniques, and leverage a dynamic load-balancing scheme to relax synchronization and improve scalability. In addition, we employ GPUs to accelerate the distributed implementation. We evaluate the highly optimized distributed beam longitudinal dynamics simulator in a supercomputing system and demonstrate speedups of more than two orders of magnitude when run on 32 GPU platforms, w.r.t. the previous state-of-art. By driving a wide range of new studies, the proposed high performance beam longitudinal dynamics simulator forms an invaluable tool for accelerator physicists.
Keyword:
Radio frequency
Particle beams
Synchrotrons
Physics
Computational modeling
Voltage
Large Hadron Collider
Distributed systems
GPU acceleration
approximate computing
network traffic optimisation
accelerator physics

期刊

IEEE Transactions on Parallel and Distributed Systems 封面图
IEEE Transactions on Parallel and Distributed Systems
IF:
6
论文数:
5.2K
被引数:
1.1W

机构

H
harokopio university athens
学者数:
2.1K
论文数: 1.7K
被引数: 16
N
National Technical University of Athens
学者数:
9.7K
论文数: 9.5K
被引数: 8.2K
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