| Preprint | |
| Report number | arXiv:2412.13755 |
| Title | Efficient high performance computing with the ALICE Event Processing Nodes GPU-based farm |
| Author(s) | Ronchetti, Federico (CERN ; Frascati) ; Akishina, Valentina (Frankfurt U., FIAS) ; Andreassen, Edvard (CERN) ; Bluhme, Nora (Frankfurt U., FIAS) ; Dange, Gautam (Frankfurt U., FIAS) ; de Cuveland, Jan (Frankfurt U., FIAS) ; Erba, Giada (CERN) ; Gaur, Hari (Frankfurt U., FIAS) ; Hutter, Dirk (Frankfurt U., FIAS) ; Kozlov, Grigory (Darmstadt, GSI) ; Krčál, Luboš (CERN) ; La Pointe, Sarah (Frankfurt U., FIAS) ; Lehrbach, Johannes (Frankfurt U., FIAS) ; Lindenstruth, Volker (Frankfurt U., FIAS ; Frankfurt U., Inst. Kernphys. ; Darmstadt, GSI) ; Neskovic, Gvozden (Frankfurt U., FIAS) ; Redelbach, Andreas (Frankfurt U., FIAS) ; Rohr, David (CERN) ; Weiglhofer, Felix (Frankfurt U., FIAS) ; Wilhelmi, Alexander (Frankfurt U., FIAS) |
| Document contact | Contact: arXiv |
| Imprint | 2024-12-18 |
| Number of pages | 12 |
| Subject category | hep-ex ; Particle Physics - Experiment |
| Abstract | 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. |
| Other source | Inspire |
| Copyright/License | preprint: (License: CC BY 4.0) |
Zapis kreiran 2024-12-20, zadnja izmjena 2024-12-21
