Speaker
Martin Vala
(Pavol Jozef Safarik University (SK))
Description
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.
Author
Martin Vala
(Pavol Jozef Safarik University (SK))