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ATLAS Distributed Computing perspectives for Run-2 Simone Campana CERN-IT/SDC o n behalf of ADC

ATLAS Distributed Computing perspectives for Run-2 Simone Campana CERN-IT/SDC o n behalf of ADC. The Challenges of Run2. LHC operation Trigger rate 1 kHz (~400) Pile-up up above 30 (~20) 25 ns bunch spacing (~50) Centre-of-mass energy x ~2 Different detector

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ATLAS Distributed Computing perspectives for Run-2 Simone Campana CERN-IT/SDC o n behalf of ADC

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  1. ATLAS Distributed Computing perspectives for Run-2 Simone Campana CERN-IT/SDC on behalf of ADC

  2. The Challenges of Run2 • LHC operation • Trigger rate 1 kHz (~400) • Pile-up up above 30 (~20) • 25 ns bunch spacing (~50) • Centre-of-mass energy x ~2 • Different detector • Constraints of ‘flat budget’ • Both for hardware and for operation and development • Data from Run1 Simone.Campana@cern.ch – WLCG Workshop

  3. Where to optimize? CPU Consumption Disk usage at T1s & T2s • Simulation • CPU • Reconstruction • CPU, Memory • Analysis • CPU, Disk Space Simone.Campana@cern.ch – WLCG Workshop

  4. Simulation • More events per 12h job, larger output files, less transfers/merging, less I/O • Or shorter, more granular jobs for opportunistic resources • Simulationis CPU intensive • Integrated Simulation Framework (ISF) • Mixing of full GEANT & fast simulation within an event • Baseline for MC production Simone.Campana@cern.ch – WLCG Workshop

  5. Reconstruction • Reconstruction is memory eager • And requires non negligible CPU • AthenaMP default from 2014 • (Almost) all production run on multicore • Analysis on single core • Optimization in code and algorithms Simone.Campana@cern.ch – WLCG Workshop

  6. Analysis Model Organized (trains) Chaotic Organized (reprocessing) • Common analysis data format: xAOD • replacement of AOD & group ntuple of any kind • Readable both by Athena & ROOT • Data reduction framework • AthenaMP to produce group data sample • Centrally via Prodsys • Based on train model • one input, N outputs • from PB to TB Simone.Campana@cern.ch – WLCG Workshop

  7. Computing Model: data processing • Optional extension of first pass processing from T0 to T1s • More flexible usage of resources • Reduce the different between T1s and T2s in the model • Loosen the job-to-data colocation • Still one/two full reprocessing from RAW / year, but multiple AOD2AOD reprocessings/year • Derivation Framework (train model) for analysis datasets Need More Flexibility Some T2s are equivalent to T1s in term of disk storage & CPU power Simone.Campana@cern.ch – WLCG Workshop

  8. Computing Model: Run-2 data placement T0/T1 DATADISK • Dynamic data placement and reduction of secondary (cache) replicas will continue • More data will be stored on tape beside disk • Only popular primary (pinned) dataset will be kept on disk after some time • All datasets will have a lifetime (from both disk and tape) • Access to data on tape still “organized” and not “chaotic” pinned T2 DATADISK pinned cache Simone.Campana@cern.ch – WLCG Workshop

  9. Preparation for Run-2 • Alessandro will now present the work being done in preparation for Run-2 • We will have a 3 days Jamboree (Dec 3-5) at CERN focused on sites related aspects • Placeholder agenda at https://indico.cern.ch/event/276502/ Simone.Campana@cern.ch – WLCG Workshop

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