Abstract
With the expected data volume increase for HL-LHC and the even more complex computing challenges set by future colliders, the need for efficient data storage and processing becomes more pressing. ROOT’s next-generation data format and I/O subsystem, RNTuple, is designed to address these challenges. RNTuple already demonstrates a clear improvement in storage and I/O efficiency, as well as overall stability and robustness with respect to its predecessor, TTree. These improvements provide a solid baseline to introduce novel extensions to common high-energy and nuclear physics (HENP) workflows. Notably, many workflows could benefit from the ability to arbitrarily join and chain data set samples at runtime, which could reduce overall storage requirements and improve application runtime and ergonomics. In this paper, we present the RNTupleProcessor, which enables HENP data set combinations with RNTuple. We will discuss the main design considerations, present the interfaces to support data set combinations and show how they integrate in typical workflows.
| Original language | English |
|---|---|
| Article number | 01013 |
| Journal | EPJ Web of Conferences |
| Volume | 337 |
| DOIs | |
| Publication status | Published - 7 Oct 2025 |
| Event | 27th International Conference on Computing in High Energy and Nuclear Physics, CHEP 2024 - AGH University of Kraków, Krakow, Poland Duration: 19 Oct 2024 → 25 Oct 2024 Conference number: 27 https://indico.cern.ch/event/1338689/ |
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