ICALEPCS2023 - List of Authors (Lacoste, D.)

Paper	Title	Page
MO3AO01	Optimisation of the Touschek Lifetime in Synchrotron Light Sources Using Badger	108
	S.M. Liuzzo, N. Carmignani, L.R. Carver, L. Hoummi, D. Lacoste, A. Le Meillour, T.P. Perron, S.M. White ESRF, Grenoble, France I.V. Agapov, M. Böse, J. Keil, L. Malina, E.S.H. Musa, B. Veglia DESY, Hamburg, Germany A.L. Edelen, P. Raimondi, R.J. Roussel, Z. Zhang SLAC, Menlo Park, California, USA T. Hellert LBNL, Berkeley, California, USA
	Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871072 Badger* is a software designed to easily access several optimizers (simplex, RCDS*, bayesian optimization, etc.) to solve a given multidimensional minimization/maximization task. The Badger software is very flexible and easy to adapt to different facilities. In the framework of the EURIZON European project Badger was used for the EBS and PETRAIII storage rings interfacing with the Tango and TINE control system. Among other tests, the optimisations of Touschek lifetime was performed and compared with the results obtained with existing tools during machine dedicated times. Z. Zhang et al., "Badger: The Missing Optimizer in ACR", doi:10.18429/JACoW-IPAC2022-TUPOST058 ** X. Huang, "Robust simplex algorithm for online optimization", 10.1103/PhysRevAccelBeams.21.104601
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-MO3AO01
About •	Received ※ 28 September 2023 — Revised ※ 08 October 2023 — Accepted ※ 13 October 2023 — Issued ※ 27 October 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TH1BCO03	The Tango Controls Collaboration Status in 2023	1100
	T. Juerges SKAO, Macclesfield, United Kingdom G. Abeillé SOLEIL, Gif-sur-Yvette, France R.J. Auger-Williams OSL, St Ives, Cambridgeshire, United Kingdom B. Bertrand, V. Hardion, A.F. Joubert MAX IV Laboratory, Lund University, Lund, Sweden R. Bourtembourg, A. Götz, D. Lacoste, N. Leclercq ESRF, Grenoble, France T. Braun byte physics, Annaburg, Germany G. Cuní, C. Pascual-Izarra, S. Rubio-Manrique ALBA-CELLS, Cerdanyola del Vallès, Spain Yu. Matveev DESY, Hamburg, Germany M. Nabywaniec, T.R. Noga, Ł. Żytniak S2Innovation, Kraków, Poland L. Pivetta Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
	Since 2021 the Tango Controls collaboration has improved and optimised its efforts in many areas. Not only have Special Interest Group meetings (SIGs) been introduced to speed up the adoption of new technologies or improvements, the kernel has switched to a fixed six-month release cycle for quicker adoption of stable kernel versions by the community. CI/CD provides now early feedback on test failures and compatibility issues. Major code refactoring allowed for a much more efficient use of developer resources. Relevant bug fixes, improvements and new features are now adopted at a much higher rate than ever before. The community participation has also noticeably improved. The kernel switched to C++14 and the logging system is undergoing a major refactoring. Among many new features and tools is jupyTango, Jupyter Notebooks on Tango Controls steroids. PyTango is now easy to install via binary wheels, old Python versions are no longer supported, the build-system is switching to CMake, and releases are now made much closer to stable cppTango releases.
	Slides TH1BCO03 [1.357 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TH1BCO03
About •	Received ※ 05 October 2023 — Revised ※ 24 October 2023 — Accepted ※ 21 November 2023 — Issued ※ 13 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THMBCMO01	New Developements on HDB++, the High-performance Data Archiving for Tango Controls	1190
	D. Lacoste, R. Bourtembourg ESRF, Grenoble, France J. Forsberg MAX IV Laboratory, Lund University, Lund, Sweden T. Juerges SKAO, Macclesfield, United Kingdom J.J.D. Mol ASTRON, Dwingeloo, The Netherlands L. Pivetta, G. Scalamera Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy S. Rubio-Manrique ALBA-CELLS, Cerdanyola del Vallès, Spain
	The Tango HDB++ project is a high performance event-driven archiving system which stores data with micro-second resolution timestamps. HDB++ supports many different backends, including MySQL/MariaDB, TimeScaleDB (a time-series PostgreSQL extension), and soon SQLite. Building on its flexible design, latest developments made supporting new backends even easier. HDB++ keeps improving with new features such as batch insertion and by becoming easier to install or setup in a testing environment, using ready to use docker images and striving to simplify all the steps of deployment. The HDB++ project is not only a data storage installation, but a full ecosystem to manage data, query it, and get the information needed. In this effort a lot of tools were developed to put a powerful backend to its proper use and be able to get the best out of the stored data. In this paper we will present as well the latest developments in data extraction, from low level libraries to web viewer integration such as grafana. Pointing out strategies in use in terms of data decimation, compression and others to help deliver data as fast as possible.
	Slides THMBCMO01 [0.926 MB]
	Poster THMBCMO01 [0.726 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-THMBCMO01
About •	Received ※ 05 October 2023 — Revised ※ 24 October 2023 — Accepted ※ 08 December 2023 — Issued ※ 16 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Optimisation of the Touschek Lifetime in Synchrotron Light Sources Using Badger

108

S.M. Liuzzo, N. Carmignani, L.R. Carver, L. Hoummi, D. Lacoste, A. Le Meillour, T.P. Perron, S.M. White
ESRF, Grenoble, France
I.V. Agapov, M. Böse, J. Keil, L. Malina, E.S.H. Musa, B. Veglia
DESY, Hamburg, Germany
A.L. Edelen, P. Raimondi, R.J. Roussel, Z. Zhang
SLAC, Menlo Park, California, USA
T. Hellert
LBNL, Berkeley, California, USA

Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871072
Badger* is a software designed to easily access several optimizers (simplex, RCDS**, bayesian optimization, etc.) to solve a given multidimensional minimization/maximization task. The Badger software is very flexible and easy to adapt to different facilities. In the framework of the EURIZON European project Badger was used for the EBS and PETRAIII storage rings interfacing with the Tango and TINE control system. Among other tests, the optimisations of Touschek lifetime was performed and compared with the results obtained with existing tools during machine dedicated times.
* Z. Zhang et al., "Badger: The Missing Optimizer in ACR", doi:10.18429/JACoW-IPAC2022-TUPOST058
** X. Huang, "Robust simplex algorithm for online optimization", 10.1103/PhysRevAccelBeams.21.104601

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-MO3AO01

About •

Received ※ 28 September 2023 — Revised ※ 08 October 2023 — Accepted ※ 13 October 2023 — Issued ※ 27 October 2023

Cite •

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

TH1BCO03

The Tango Controls Collaboration Status in 2023

1100

T. Juerges
SKAO, Macclesfield, United Kingdom
G. Abeillé
SOLEIL, Gif-sur-Yvette, France
R.J. Auger-Williams
OSL, St Ives, Cambridgeshire, United Kingdom
B. Bertrand, V. Hardion, A.F. Joubert
MAX IV Laboratory, Lund University, Lund, Sweden
R. Bourtembourg, A. Götz, D. Lacoste, N. Leclercq
ESRF, Grenoble, France
T. Braun
byte physics, Annaburg, Germany
G. Cuní, C. Pascual-Izarra, S. Rubio-Manrique
ALBA-CELLS, Cerdanyola del Vallès, Spain
Yu. Matveev
DESY, Hamburg, Germany
M. Nabywaniec, T.R. Noga, Ł. Żytniak
S2Innovation, Kraków, Poland
L. Pivetta
Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy

Since 2021 the Tango Controls collaboration has improved and optimised its efforts in many areas. Not only have Special Interest Group meetings (SIGs) been introduced to speed up the adoption of new technologies or improvements, the kernel has switched to a fixed six-month release cycle for quicker adoption of stable kernel versions by the community. CI/CD provides now early feedback on test failures and compatibility issues. Major code refactoring allowed for a much more efficient use of developer resources. Relevant bug fixes, improvements and new features are now adopted at a much higher rate than ever before. The community participation has also noticeably improved. The kernel switched to C++14 and the logging system is undergoing a major refactoring. Among many new features and tools is jupyTango, Jupyter Notebooks on Tango Controls steroids. PyTango is now easy to install via binary wheels, old Python versions are no longer supported, the build-system is switching to CMake, and releases are now made much closer to stable cppTango releases.

Slides TH1BCO03 [1.357 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TH1BCO03

About •

Received ※ 05 October 2023 — Revised ※ 24 October 2023 — Accepted ※ 21 November 2023 — Issued ※ 13 December 2023

Cite •

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

THMBCMO01

New Developements on HDB++, the High-performance Data Archiving for Tango Controls

1190

D. Lacoste, R. Bourtembourg
ESRF, Grenoble, France
J. Forsberg
MAX IV Laboratory, Lund University, Lund, Sweden
T. Juerges
SKAO, Macclesfield, United Kingdom
J.J.D. Mol
ASTRON, Dwingeloo, The Netherlands
L. Pivetta, G. Scalamera
Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
S. Rubio-Manrique
ALBA-CELLS, Cerdanyola del Vallès, Spain

The Tango HDB++ project is a high performance event-driven archiving system which stores data with micro-second resolution timestamps. HDB++ supports many different backends, including MySQL/MariaDB, TimeScaleDB (a time-series PostgreSQL extension), and soon SQLite. Building on its flexible design, latest developments made supporting new backends even easier. HDB++ keeps improving with new features such as batch insertion and by becoming easier to install or setup in a testing environment, using ready to use docker images and striving to simplify all the steps of deployment. The HDB++ project is not only a data storage installation, but a full ecosystem to manage data, query it, and get the information needed. In this effort a lot of tools were developed to put a powerful backend to its proper use and be able to get the best out of the stored data. In this paper we will present as well the latest developments in data extraction, from low level libraries to web viewer integration such as grafana. Pointing out strategies in use in terms of data decimation, compression and others to help deliver data as fast as possible.

Slides THMBCMO01 [0.926 MB]

Poster THMBCMO01 [0.726 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-THMBCMO01

About •

Received ※ 05 October 2023 — Revised ※ 24 October 2023 — Accepted ※ 08 December 2023 — Issued ※ 16 December 2023

Cite •

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