FORM, a Fine-grained Object Reading/Writing Model for DUNE
DUNE’s current processing framework ( art ) was branched from the event processing framework of CMS, a collider-physics experiment. Therefore art is built on event-based concepts as its fundamental processing unit. The “event” concept is not always helpful for neutrino experiments, such as DUNE. In DUNE, each event is represented by a trigger record, which can be much larger than a typical collider event — often several gigabytes, compared to just megabytes for collider events. To avoid allocating large chunks of memory due to the large and complex nature of DUNE’s events, the experiment is developing a framework (Phlex) that is able to break apart trigger records into smaller segments for more granular processing, and then stitch those chunks back together into an event. For an event-processing framework to function efficiently, it must be integrated with an input/output (I/O) system that supports fine-grained data handling. FORM (Fine-grained Object Reading/Writing Model) is a DUNE project focused on developing a data storage and I/O system that enables information to be written and accessed in smaller, more manageable units supporting framework that perform fine-grained event processing. To support fine-grained processing, data objects are partitioned into segments and stored separately in accessible locations. This approach allows the I/O system to read and write individual segments, avoiding the high memory usage that comes from handling large monolithic data objects. The complexity of data storage and I/O operations is encapsulated within the FORM infrastructure, making it transparent to client-side components like processing algorithms. By writing and reading multiple smaller entries as discrete events, FORM improves concurrency and scalability in the data processing pipeline.
Authors
- P. van Gemmeren (ORCID: https://orcid.org/0000-0002-7227-4006)
- Michael Kirby
- Barnali Chowdhury
Institutions
- Argonne National Laboratory (US)
- Brookhaven National Laboratory (US)
Publication Details
- Journal
- EPJ Web of Conferences
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1051/epjconf/202533701318
- Primary Topic
- Computational Physics and Python Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- U.S. Department of Energy
- CERN
- UK Research and Innovation
- Royal Society
- European Commission
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
- Centre National de la Recherche Scientifique
- Ministerio de Ciencia e Innovación
- Fundação de Amparo à Pesquisa do Estado de Goiás
- Xunta de Galicia
- Junta de Andalucía
- Office of Science
- Horizon 2020 Framework Programme
- Natural Sciences and Engineering Research Council of Canada
- Science and Technology Facilities Council
- Fundação para a Ciência e a Tecnologia
- European Regional Development Fund
- Institut National de Physique Nucléaire et de Physique des Particules
- High Energy Physics
- Argonne National Laboratory
- Fermilab
- Brookhaven National Laboratory