Cargando…
Experience and performance of persistent memory for the DUNE data acquisition system
Emerging high-performance storage technologies are opening up the possibility of designing new distributed data acquisition (DAQ) system architectures, in which the live acquisition of data and their processing are decoupled through a storage element. An example of these technologies is 3D XPoint, w...
Autores principales: | , , |
---|---|
Lenguaje: | eng |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://dx.doi.org/10.1109/TNS.2021.3084848 http://cds.cern.ch/record/2746226 |
_version_ | 1780968787934183424 |
---|---|
author | Abed Abud, Adam Miotto, Giovanna Lehmann Sipos, Roland |
author_facet | Abed Abud, Adam Miotto, Giovanna Lehmann Sipos, Roland |
author_sort | Abed Abud, Adam |
collection | CERN |
description | Emerging high-performance storage technologies are opening up the possibility of designing new distributed data acquisition (DAQ) system architectures, in which the live acquisition of data and their processing are decoupled through a storage element. An example of these technologies is 3D XPoint, which promises to fill the gap between memory and traditional storage and offers unprecedented high throughput for nonvolatile data. In this article, we characterize the performance of persistent memory devices that use the 3D XPoint technology, in the context of the DAQ system for one large Particle Physics experiment, DUNE. This experiment must be capable of storing, upon a specific signal, incoming data for up to 100 s, with a throughput of 1.5 TB/s, for an aggregate size of 150 TB. The modular nature of the apparatus allows splitting the problem into 150 identical units operating in parallel, each at 10 GB/s. The target is to be able to dedicate a single CPU to each of those units for DAQ and storage. |
id | cern-2746226 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2020 |
record_format | invenio |
spelling | cern-27462262023-03-14T19:07:05Zdoi:10.1109/TNS.2021.3084848http://cds.cern.ch/record/2746226engAbed Abud, AdamMiotto, Giovanna LehmannSipos, RolandExperience and performance of persistent memory for the DUNE data acquisition systemphysics.ins-detDetectors and Experimental TechniquesEmerging high-performance storage technologies are opening up the possibility of designing new distributed data acquisition (DAQ) system architectures, in which the live acquisition of data and their processing are decoupled through a storage element. An example of these technologies is 3D XPoint, which promises to fill the gap between memory and traditional storage and offers unprecedented high throughput for nonvolatile data. In this article, we characterize the performance of persistent memory devices that use the 3D XPoint technology, in the context of the DAQ system for one large Particle Physics experiment, DUNE. This experiment must be capable of storing, upon a specific signal, incoming data for up to 100 s, with a throughput of 1.5 TB/s, for an aggregate size of 150 TB. The modular nature of the apparatus allows splitting the problem into 150 identical units operating in parallel, each at 10 GB/s. The target is to be able to dedicate a single CPU to each of those units for DAQ and storage.Emerging high-performance storage technologies are opening up the possibility of designing new distributed data acquisition system architectures, in which the live acquisition of data and their processing are decoupled through a storage element. An example of these technologies is 3DXPoint, which promises to fill the gap between memory and traditional storage and offers unprecedented high throughput for data persistency. In this paper, we characterize the performance of persistent memory devices, which use the 3DXPoint technology, in the context of the data acquisition system for one large Particle Physics experiment, DUNE. This experiment must be capable of storing, upon a specific signal, incoming data for up to 100 seconds, with a throughput of 1.5 TB/s, for an aggregate size of 150 TB. The modular nature of the apparatus allows splitting the problem into 150 identical units operating in parallel, each at 10 GB/s. The target is to be able to dedicate a single CPU to each of those units for data acquisition and storage.arXiv:2011.01341oai:cds.cern.ch:27462262020-10-30 |
spellingShingle | physics.ins-det Detectors and Experimental Techniques Abed Abud, Adam Miotto, Giovanna Lehmann Sipos, Roland Experience and performance of persistent memory for the DUNE data acquisition system |
title | Experience and performance of persistent memory for the DUNE data acquisition system |
title_full | Experience and performance of persistent memory for the DUNE data acquisition system |
title_fullStr | Experience and performance of persistent memory for the DUNE data acquisition system |
title_full_unstemmed | Experience and performance of persistent memory for the DUNE data acquisition system |
title_short | Experience and performance of persistent memory for the DUNE data acquisition system |
title_sort | experience and performance of persistent memory for the dune data acquisition system |
topic | physics.ins-det Detectors and Experimental Techniques |
url | https://dx.doi.org/10.1109/TNS.2021.3084848 http://cds.cern.ch/record/2746226 |
work_keys_str_mv | AT abedabudadam experienceandperformanceofpersistentmemoryforthedunedataacquisitionsystem AT miottogiovannalehmann experienceandperformanceofpersistentmemoryforthedunedataacquisitionsystem AT siposroland experienceandperformanceofpersistentmemoryforthedunedataacquisitionsystem |