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arXiv:2208.02382 (physics)
[Submitted on 3 Aug 2022 (v1), last revised 9 Aug 2022 (this version, v2)]

Title:Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale

Authors:Axel Huebl, Remi Lehe, Chad E. Mitchell, Ji Qiang, Robert D. Ryne, Ryan T. Sandberg, Jean-Luc Vay
View a PDF of the paper titled Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale, by Axel Huebl and 6 other authors
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Abstract:Particle accelerators are among the largest, most complex devices. To meet the challenges of increasing energy, intensity, accuracy, compactness, complexity and efficiency, increasingly sophisticated computational tools are required for their design and optimization. It is key that contemporary software take advantage of the latest advances in computer hardware and scientific software engineering practices, delivering speed, reproducibility and feature composability for the aforementioned challenges. A new open source software stack is being developed at the heart of the Beam pLasma Accelerator Simulation Toolkit (BLAST) by LBNL and collaborators, providing new particle-in-cell modeling codes capable of exploiting the power of GPUs on Exascale supercomputers. Combined with advanced numerical techniques, such as mesh-refinement, and intrinsic support for machine learning, these codes are primed to provide ultrafast to ultraprecise modeling for future accelerator design and operations.
Comments: 4 pages, 8 figures; NAPAC22, Invited Oral, TUYE2
Subjects: Accelerator Physics (physics.acc-ph); Distributed, Parallel, and Cluster Computing (cs.DC)
MSC classes: 78-10
ACM classes: I.6.0; D.2.12; D.2.13
Cite as: arXiv:2208.02382 [physics.acc-ph]
  (or arXiv:2208.02382v2 [physics.acc-ph] for this version)
  https://doi.org/10.48550/arXiv.2208.02382
arXiv-issued DOI via DataCite
Journal reference: NAPAC22, 2022
Related DOI: https://doi.org/10.18429/JACoW-NAPAC2022-TUYE2
DOI(s) linking to related resources

Submission history

From: Axel Huebl [view email]
[v1] Wed, 3 Aug 2022 23:34:40 UTC (234 KB)
[v2] Tue, 9 Aug 2022 14:45:41 UTC (234 KB)
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