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TUSDSC01 |
BLISS: ESRF All-In-One, Python-based Experiment Control System | |
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| BLISS is an all-in-one experiment control system designed to address the complex challenges of synchronized data acquisition and management, for synchrotrons and other labs. Written in Python, BLISS provides a comprehensive solution for hardware control (BLISS native, Tango and EPICS control systems are supported), experiment control sequences, data acquisition, and data visualization. Its modular design makes it easy to configure and customize for different setups. One of the key features of BLISS is its decoupling of data acquisition from data storage, which is achieved through the use of Redis as a temporary buffer. Thanks to a companion Python library called "blissdata" clients can access data without perturbing the acquisition, alleviating real-time constraints for display, saving or to perform online data analysis. On top of blissdata, BLISS is shipped with Flint, a powerful data visualization tool to display and interact with experimental data in real-time, providing an efficient solution for quality control and immediate feedback. BLISS comes with handy web applications, including a configuration tool and a web terminal ; users can easily configure the system and interact with it. It is designed to interface with Daiquiri, for more advanced web applications. Additionally, BLISS includes a full simulation environment, which can be used to learn about the system and to try it out. In summary, BLISS is a complete solution for laboratory data acquisition and management that provides a user-friendly interface and supports online data analysis and data display. | ||
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Poster TUSDSC01 [2.538 MB] | |
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |
| THMBCMO31 | LImA2: Edge Distributed Acquisition and Processing Framework for High Performance 2D Detectors | 1269 |
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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 |
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Slides THMBCMO31 [0.572 MB] | |
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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) | |