About
This contribution presents the NDMSPC (N-Dimensional Space) framework, designed for efficient management and analysis of high-dimensional datasets within the CERN EOS environment. We explore the integration of ROOT's THnSparse for memory-efficient multi-dimensional histogramming alongside TTree for robust data storage. By leveraging EOS as the underlying storage layer, the framework achieves the high-throughput I/O necessary for processing exa-scale sparse data. The discussion focuses on optimization techniques for data layout and access patterns that maximize performance when handling complex N-dimensional physics structures in a distributed storage architecture.
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The talk
Credits and licence
- Institution
- CERN
- Speaker
- Martin Vala
- Year
- 2026
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed