| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01087 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701087 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701087
SYCL-based online data processing framework concept for PANDA
1 Department of Information Technologies, Jagiellonian University, Prof. Łojasiewicza 11, Kraków, Poland
2 Doctoral School of Exact and Natural Sciences, Jagiellonian University, Prof. Łojasiewicza 11, Kraków, Poland
* e-mail: bartosz.sobol@doctoral.uj.edu.pl
** e-mail: grzegorz.korcyl@uj.edu.pl
Published online: 7 October 2025
The PANDA experiment has been designed to incorporate software triggers and online data processing. Although PANDA may not surpass the largest experiments in terms of raw data rates, designing and developing the processing pipeline and software platform for this purpose is still a challenge. Given the uncertain timeline for PANDA and the constantly evolving landscape of computing hardware, our attention is directed toward ensuring the futureproofness of the solutions we develop.
The PandaR2 is a concept for a framework handling online data processing in heterogeneous and distributed HPC environments. It utilizes the SYCL programming model as the primary technology for parallelization and offloading. Being a new and standalone entity, PandaR2 also interfaces with the PANDA’s original ROOT-based simulation and analysis framework - PandaRoot, connecting the best of both worlds.
This contribution aims to present an overview of the PandaR2 SYCL-centric architecture. We will share experiences with SYCL during the codebase design process, particularly highlighting its portability across various hardware platforms and compilers. Additionally, we will showcase the performance results of the initial algorithms implemented in PandaR2.
© The Authors, published by EDP Sciences, 2025
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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