Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
---|---|---|
Article Number | 09002 | |
Number of page(s) | 9 | |
Section | 9 - Exascale Science | |
DOI | https://doi.org/10.1051/epjconf/202024509002 | |
Published online | 16 November 2020 |
https://doi.org/10.1051/epjconf/202024509002
Integrating LHCb workflows on HPC resources: status and strategies
1
CERN, EP Department, Geneva, Switzerland
2
CERN, IT Department, Geneva, Switzerland
3
NRC Kurchatov Institute”, IHEP, Protvino, Russia
* e-mail: federico.stagni@cern.ch
Published online: 16 November 2020
High Performance Computing (HPC) supercomputers are expected to play an increasingly important role in HEP computing in the coming years. While HPC resources are not necessarily the optimal fit for HEP workflows, computing time at HPC centers on an opportunistic basis has already been available to the LHC experiments for some time, and it is also possible that part of the pledged computing resources will be offered as CPU time allocations at HPC centers in the future. The integration of the experiment workflows to make the most efficient use of HPC resources is therefore essential. This paper describes the work that has been necessary to integrate LHCb workflows at a specific HPC site, the Marconi-A2 system at CINECA in Italy, where LHCb benefited from a joint PRACE (Partnership for Advanced Computing in Europe) allocation with the other Large Hadron Collider (LHC) experiments. This has required addressing two types of challenges: on the software application workloads, for optimising their performance on a many-core hardware architecture that differs significantly from those traditionally used in WLCG (Worldwide LHC Computing Grid), by reducing memory footprint using a multi-process approach; and in the distributed computing area, for submitting these workloads using more than one logical processor per job, which had never been done yet in LHCb.
© The Authors, published by EDP Sciences, 2020
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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