RNTuple: Towards First-Class Support for HPC data centers
| Autor: | Miotto, Giovanna Lazzari, Lopez-Gomez, Javier |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | Compared to LHC Run 1 and Run 2, future HEP experiments, e.g., at the HL-LHC, will increase the volume of generated data by an order of magnitude. In order to sustain the expected analysis throughput, ROOT's RNTuple I/O subsystem has been engineered to overcome the bottlenecks of the TTree I/O subsystem, focusing also on a compact data format, asynchronous and parallel requests, and a layered architecture that allows supporting distributed filesystem-less storage systems, e.g. HPC-oriented object stores. In a previous publication, we introduced and evaluated the RNTuple's native backend for Intel DAOS. Since its first prototype, we carried out a number of improvements both on RNTuple and its DAOS backend aiming to saturate the physical link, such as support for vector writes and an improved RNTuple-to-DAOS mapping, only to name a few. In parallel, the latest developments allow for better integration between RNTuple and ROOT's storage-agnostic, declarative interface to write HEP analyses, RDataFrame. In this work, we contribute with the following: (i) a redesign of the RNTuple DAOS backend, including a mechanism for efficient population of the object store based on existing data; and (ii) an experimental evaluation on a single-node platform, showing a significant increase in the analysis throughput for typical HEP workflows. Comment: 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022), 5 pages |
| Databáze: | arXiv |
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