# Optimizing High-Dimensional Data Storage: NDMSPC with THnSparse and TTree on EOS

- Institution: CERN
- Speaker: Martin Vala
- Year: 2026
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3023541)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

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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## Lectures

1. [Optimizing High-Dimensional Data Storage: NDMSPC with THnSparse and TTree on EOS](https://invisiblenetwork.ai/learn/cern/3023541/d1f70fe2fa5e), 24 min (CERN's player)