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Unleashing High-Energy Physics Discoveries: FermiLab Data Handling Strategy

Explore how FermiLab manages massive data volumes and collaborations in high-energy physics experiments, optimizing resource usage and grid standards. Learn about data processing advancements and success stories in this dynamic field.

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Unleashing High-Energy Physics Discoveries: FermiLab Data Handling Strategy

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  1. High Energy Physics @ FermiLab • Two physics detectors (5 stories tall each) to understand smallest scale of matter • Each experiment has ~500 people doing science • Each experiment handles millions of particle collisions per second - • HUGE amount of data!

  2. Total 4.3 petabytes Data volume and analysis rates • Analysis • >1 petabyte/month

  3. Use resources at participating institutions Ship and analyze data around the world ! Get data fast, processes it, and then immediately store the results back at Fermilab The data challenge

  4. Strategy • Common business model for data cataloguing, tracking, and mining. • Streamlined support of the underlying machinery • Shared expertise solves issues at the user level. • Follow grid standards, use grid middleware and shared resources (OSG and LCG grids). • Contribute to grid projects • OSG resource selection, SRM, security…

  5. How we handle data • Sequential access via Metadata ( SAM ) • data storage, directly from the detector or from remote data processing facilities • data cataloguing, mining • distributed resources management to optimize usage and data throughput, and enforce the policies of the experiments. • Use of variety storage service providers • dCache, enstore, HPSS, SRM, in house disk resources

  6. LCG grid 5Tb OSG grid 20Tb Westgrid 10Tb Fermi 40Tb IN2P3 10Tb Success story: D0 refixing • Problem. Correct processing mistake in 6 weeks • 85 Tb and 4Million hours of 1Ghz CPU time • Plenty of network but no free CPU to do the job at Fermilab • Solution. Involve CPU resources elsewhere. • Ship detector data directly to the analysis • Cache re-usable data near computing sites

  7. Conclusion • Store a petabyte a year • Process a petabyte a month • Computing that meets growing demands of the HEP experiments • one step ahead of the physics needs

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