Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
---|---|---|
Article Number | 11006 | |
Number of page(s) | 9 | |
Section | Heterogeneous Computing and Accelerators | |
DOI | https://doi.org/10.1051/epjconf/202429511006 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429511006
Enabling INFN–T1 to support heterogeneous computing architectures
1 INFN–CNAF, v.le B. Pichat 6/2 - 40127 Bologna, IT
2 INFN Sezione di Perugia, Via Alessandro Pascoli, 06123 Perugia, IT
3 Bologna University and INFN Sezione di Bologna, v.le B. Pichat 6/2 - 40127 Bologna, IT
4 INFN Sezione di Pisa, Largo B. Pontecorvo 3, 56127 Pisa, IT
5 E4 Computer Engineering SPA, Viale Martiri della Libertà 66, Scandiano, IT
* e-mail: dalpra@infn.it
** e-mail: daniele.spiga@pg.infn.it
Published online: 6 May 2024
The INFN–CNAF Tier-1 located in Bologna (Italy) is a center of the WLCG e-Infrastructure providing computing power to the four major LHC collaborations and also supports the computing needs of about fifty more groups - also from non HEP research domains. The CNAF Tier1 center has been historically very active putting effort in the integration of computing resources, proposing and prototyping solutions both for extension through Cloud resources, public and private, and with remotely owned sites, as well as developing an integrated HTC+HPC system with the PRACE CINECA supercomputer center located 8Km far from the CNAF Tier-1 located in Bologna. In order to meet the requirements for the new Tecnopolo center, where the CNAF Tier-1 will be hosted, the resource integration activities keep progressing. In particular, this contribution will detail the challenges that have recently been addressed, providing opportunistic access to non standard CPU architectures, such as PowerPC and hardware accelerators (GPUs). We explain the approach adopted to both transparently provision x86_64, ppc64le and NVIDIA V100 GPUs from the Marconi 100 HPC cluster managed by CINECA and to access data from the Tier1 storage system at CNAF. The solution adopted is general enough to enable seamless integration of other computing architectures at the same time from different providers, such as ARM CPUs from the TEXTAROSSA project, and we report about the integration of these within the computing model of the CMS experiment. Finally we will discuss the results of the early experience.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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