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May 8 – 12, 2023
Norfolk Waterside Marriott
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Storing LHC Data in Amazon S3 and Intel DAOS through RNTuple

May 9, 2023, 11:30 AM
Norfolk Ballroom III-V (Norfolk Waterside Marriott)

Norfolk Ballroom III-V

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 1 - Data and Metadata Organization, Management and Access Track 1 - Data and Metadata Organization, Management and Access


Ms Lazzari Miotto, Giovanna (UFRGS (BR))


Current and future distributed HENP data analysis infrastructures rely increasingly on object stores in addition to regular remote file systems. Such file-less storage systems are popular as a means to escape the inherent scalability limits of the POSIX file system API. Cloud storage is already dominated by S3-like object stores, and HPC sites are starting to take advantage of object stores for the next generation of supercomputers. In light of this, ROOT's new I/O subsystem RNTuple has been engineered to support object stores alongside (distributed) file systems as first class citizens, while also addressing performance bottlenecks and interface shortcomings of its predecessor, TTree I/O.
In this contribution, we describe the improvements around RNTuple’s support for object stores, expounding on the challenges and insights toward efficient storage and high-throughput data transfers. Specifically, we introduce RNTuple’s native backend for the Amazon S3 cloud storage and present the latest developments in our Intel DAOS backend, demonstrating RNTuple’s integration with next-generation HPC sites.
Through experimental evaluations, we compare the two backends in single node and distributed end-to-end analyses using ROOT’s RDataFrame, proving Amazon S3 and Intel DAOS as viable HENP storage providers.

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