Efficient high performance computing with the ALICE Event Processing Nodes GPU-based farm
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909432867192832 |
|---|---|
| author | Ronchetti, Federico Akishina, Valentina Andreassen, Edvard Bluhme, Nora Dange, Gautam de Cuveland, Jan Erba, Giada Gaur, Hari Hutter, Dirk Kozlov, Grigory Krčál, Luboš La Pointe, Sarah Lehrbach, Johannes Lindenstruth, Volker Neskovic, Gvozden Redelbach, Andreas Rohr, David Weiglhofer, Felix Wilhelmi, Alexander |
| author_facet | Ronchetti, Federico Akishina, Valentina Andreassen, Edvard Bluhme, Nora Dange, Gautam de Cuveland, Jan Erba, Giada Gaur, Hari Hutter, Dirk Kozlov, Grigory Krčál, Luboš La Pointe, Sarah Lehrbach, Johannes Lindenstruth, Volker Neskovic, Gvozden Redelbach, Andreas Rohr, David Weiglhofer, Felix Wilhelmi, Alexander |
| contents | Due to the increase of data volumes expected for the LHC Run 3 and Run 4, the ALICE Collaboration designed and deployed a new, energy efficient, computing model to run Online and Offline O$^2$ data processing within a single software framework. The ALICE O$^2$ Event Processing Nodes (EPN) project performs online data reconstruction using GPUs (Graphic Processing Units) instead of CPUs and applies an efficient, entropy-based, online data compression to cope with PbPb collision data at a 50 kHz hadronic interaction rate. Also, the O$^2$ EPN farm infrastructure features an energy efficient, environmentally friendly, adiabatic cooling system which allows for operational and capital cost savings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13755 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient high performance computing with the ALICE Event Processing Nodes GPU-based farm Ronchetti, Federico Akishina, Valentina Andreassen, Edvard Bluhme, Nora Dange, Gautam de Cuveland, Jan Erba, Giada Gaur, Hari Hutter, Dirk Kozlov, Grigory Krčál, Luboš La Pointe, Sarah Lehrbach, Johannes Lindenstruth, Volker Neskovic, Gvozden Redelbach, Andreas Rohr, David Weiglhofer, Felix Wilhelmi, Alexander High Energy Physics - Experiment Due to the increase of data volumes expected for the LHC Run 3 and Run 4, the ALICE Collaboration designed and deployed a new, energy efficient, computing model to run Online and Offline O$^2$ data processing within a single software framework. The ALICE O$^2$ Event Processing Nodes (EPN) project performs online data reconstruction using GPUs (Graphic Processing Units) instead of CPUs and applies an efficient, entropy-based, online data compression to cope with PbPb collision data at a 50 kHz hadronic interaction rate. Also, the O$^2$ EPN farm infrastructure features an energy efficient, environmentally friendly, adiabatic cooling system which allows for operational and capital cost savings. |
| title | Efficient high performance computing with the ALICE Event Processing Nodes GPU-based farm |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2412.13755 |