ICALEPCS2019 - List of Authors (Mexner, W.)

Paper	Title	Page
MOMPL009	Control System Virtualization at Karlsruhe Research Accelerator	143
MOPHA093	use link to see paper's listing under its alternate paper code
	W. Mexner, B. Aydt, E. Blomley, E. Bründermann, D. Hoffmann, A.-S. Müller, M. Schuh KIT, Eggenstein-Leopoldshafen, Germany S. Marsching Aquenos GmbH, Baden-Baden, Germany
	With the deployment of a storage spaces direct hyper-converged cluster in 2018, the whole control system server and network infrastructure of the Karlsruhe Research Accelerator have been virtualized to improve the control system availability. The cluster with 6 Dell PowerEdge R740Xd servers with 1.152 GB RAM, 72 cores and 40 TByte hyperconverged storage operates in total 120 virtual machines. We will report on our experiences running EPICS IOCs and the industrial control system WinCC OA in this virtual environment.
	Poster MOMPL009 [0.608 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOMPL009
About •	paper received ※ 27 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUCPL06	Accelerating Machine Learning for Machine Physics (an AMALEA-project at KIT)	781
	T. Boltz, E. Bründermann, M. Caselle, A. Kopmann, W. Mexner, A.-S. Müller, W. Wang KIT, Karlsruhe, Germany
	The German Helmholtz Innovation Pool project will explore and provide novel cutting edge Machine Learning techniques to address some of the most urgent challenges in the era of large data harvests in accelerator physics. Progress in virtually all areas of accelerator based physics research relies on recording and analyzing enormous amounts of data. This data is produced by progressively sophisticated fast detectors alongside increasingly precise accelerator diagnostic systems. As KIT contribution to AMALEA it is planned to investigate a design of a fast and adaptive feedback system that reacts to small changes in the charge distribution of the electron bunch and establishes extensive control over the longitudinal beam dynamics. As a promising and well-motivated approach, reinforcement learning methods are considered. In a second step the algorithm will be implemented as a pilot experiment to a novel PCIe FPGA readout electronics card based on Zynq UltraScale⁺ MultiProcessor System on-Chip (MPSoC).
	Slides TUCPL06 [5.955 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL06
About •	paper received ※ 27 September 2019 paper accepted ※ 01 November 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

MOMPL009

Control System Virtualization at Karlsruhe Research Accelerator

143

MOPHA093

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

W. Mexner, B. Aydt, E. Blomley, E. Bründermann, D. Hoffmann, A.-S. Müller, M. Schuh
KIT, Eggenstein-Leopoldshafen, Germany
S. Marsching
Aquenos GmbH, Baden-Baden, Germany

With the deployment of a storage spaces direct hyper-converged cluster in 2018, the whole control system server and network infrastructure of the Karlsruhe Research Accelerator have been virtualized to improve the control system availability. The cluster with 6 Dell PowerEdge R740Xd servers with 1.152 GB RAM, 72 cores and 40 TByte hyperconverged storage operates in total 120 virtual machines. We will report on our experiences running EPICS IOCs and the industrial control system WinCC OA in this virtual environment.

Poster MOMPL009 [0.608 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOMPL009

About •

paper received ※ 27 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

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

TUCPL06

Accelerating Machine Learning for Machine Physics (an AMALEA-project at KIT)

781

T. Boltz, E. Bründermann, M. Caselle, A. Kopmann, W. Mexner, A.-S. Müller, W. Wang
KIT, Karlsruhe, Germany

The German Helmholtz Innovation Pool project will explore and provide novel cutting edge Machine Learning techniques to address some of the most urgent challenges in the era of large data harvests in accelerator physics. Progress in virtually all areas of accelerator based physics research relies on recording and analyzing enormous amounts of data. This data is produced by progressively sophisticated fast detectors alongside increasingly precise accelerator diagnostic systems. As KIT contribution to AMALEA it is planned to investigate a design of a fast and adaptive feedback system that reacts to small changes in the charge distribution of the electron bunch and establishes extensive control over the longitudinal beam dynamics. As a promising and well-motivated approach, reinforcement learning methods are considered. In a second step the algorithm will be implemented as a pilot experiment to a novel PCIe FPGA readout electronics card based on Zynq UltraScale⁺ MultiProcessor System on-Chip (MPSoC).

Slides TUCPL06 [5.955 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL06

About •

paper received ※ 27 September 2019 paper accepted ※ 01 November 2019 issue date ※ 30 August 2020

Export •

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