ICALEPCS2023 - List of Authors (Kieffer, J.)

Paper	Title	Page
THMBCMO31	LImA2: Edge Distributed Acquisition and Processing Framework for High Performance 2D Detectors	1269
	S. Debionne, L. Claustre, P. Fajardo, A. Götz, A. Homs Puron, J. Kieffer, R. Ponsard ESRF, Grenoble, France
	LImA* is a framework born at the ESRF for 2D Data Acquisition (DAQ), basic Online Data Analysis (ODA) and processing with high-throughput detectors. While in production for 15 years in several synchrotron facilities, the ever-increasing detector frame rates make more and more difficult performing DAQ & ODA tasks on a single computer. LImA2 is designed to scale horizontally, using multiple hosts for DAQ & ODA. This enables more advanced strategies for data feature extraction while keeping a low latency. LImA2 separates three functional blocks: detector control, image acquisition, and data processing. A control process configures the detector, while one or more receiver processes perform the DAQ and ODA, like the generation of fast feedback signals. The detectors currently supported in LImA2 are the PSI/Jungfrau, the ESRF/Smartpix and the Dectris/Eiger2. The former performs pixel assembly and intensity correction in GPU; the second exploits RoCE capabilities; and the latter features dual threshold, multi-band images. Raw data rates up to 8 GByte/s can be handled by a single computer, scalable if necessary. In addition to a classic processing, advanced pipelines are also implemented. A Serial-MX/pyFAI* pipeline extracts diffraction peaks in GPU in order to filter low quality data. NVIDIA GPUDirect is used by a third pipeline providing 2D processing with remarkable low latency. IBM Power9 optimizations like the NX GZIP compression and the PCI-e multi-host extension are exploited. * LIMA - https://accelconf.web.cern.ch/ICALEPCS2013/papers/frcoaab08.pdf Jungfraujoch - https://doi.org/10.1107/S1600577522010268 * pyFAI - https://doi.org/10.1107/S1600576715004306
	Slides THMBCMO31 [0.572 MB]
	Poster THMBCMO31 [14.959 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-THMBCMO31
About •	Received ※ 06 October 2023 — Revised ※ 08 October 2023 — Accepted ※ 11 December 2023 — Issued ※ 13 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

LImA2: Edge Distributed Acquisition and Processing Framework for High Performance 2D Detectors

1269

S. Debionne, L. Claustre, P. Fajardo, A. Götz, A. Homs Puron, J. Kieffer, R. Ponsard
ESRF, Grenoble, France

LImA* is a framework born at the ESRF for 2D Data Acquisition (DAQ), basic Online Data Analysis (ODA) and processing with high-throughput detectors. While in production for 15 years in several synchrotron facilities, the ever-increasing detector frame rates make more and more difficult performing DAQ & ODA tasks on a single computer**. LImA2 is designed to scale horizontally, using multiple hosts for DAQ & ODA. This enables more advanced strategies for data feature extraction while keeping a low latency. LImA2 separates three functional blocks: detector control, image acquisition, and data processing. A control process configures the detector, while one or more receiver processes perform the DAQ and ODA, like the generation of fast feedback signals. The detectors currently supported in LImA2 are the PSI/Jungfrau, the ESRF/Smartpix and the Dectris/Eiger2. The former performs pixel assembly and intensity correction in GPU; the second exploits RoCE capabilities; and the latter features dual threshold, multi-band images. Raw data rates up to 8 GByte/s can be handled by a single computer, scalable if necessary. In addition to a classic processing, advanced pipelines are also implemented. A Serial-MX/pyFAI*** pipeline extracts diffraction peaks in GPU in order to filter low quality data. NVIDIA GPUDirect is used by a third pipeline providing 2D processing with remarkable low latency. IBM Power9 optimizations like the NX GZIP compression and the PCI-e multi-host extension are exploited.
* LIMA - https://accelconf.web.cern.ch/ICALEPCS2013/papers/frcoaab08.pdf
** Jungfraujoch - https://doi.org/10.1107/S1600577522010268
*** pyFAI - https://doi.org/10.1107/S1600576715004306

Slides THMBCMO31 [0.572 MB]

Poster THMBCMO31 [14.959 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-THMBCMO31

About •

Received ※ 06 October 2023 — Revised ※ 08 October 2023 — Accepted ※ 11 December 2023 — Issued ※ 13 December 2023

Cite •

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