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The Athena Control Framework in Production, New Developments and Lessons Learned

The Athena Control Framework in Production, New Developments and Lessons Learned. September 27 2004 C. Leggett , P. Calafiura, W. Lavrijsen, M. Marino, D. Quarrie. Athena and Gaudi. Gaudi framework shared by LHCb, ATLAS, GLAST, HARP, and OPERA

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The Athena Control Framework in Production, New Developments and Lessons Learned

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  1. The Athena Control Framework in Production, New Developments and Lessons Learned September 27 2004 C. Leggett, P. Calafiura, W. Lavrijsen, M. Marino, D. Quarrie

  2. Athena and Gaudi • Gaudi framework shared by LHCb, ATLAS, GLAST, HARP, and OPERA • Based on a modular component architecture, structured around dynamically loadable libraries • Maintains a separation between transient and persistent layers, allowing new technologies to replace aging ones with minimal impact on the end user • Athena comprises the ATLAS specific extensions to Gaudi, most notably: • Storegate – the data store • Interval Of Validity Svc – managing time dependent data • Pileup – combining multiple events • HistorySvc – maintaining a multi-level record • Python scripting

  3. Athena and Gaudi Application Manager Converter Converter Sequencer Converter Event LoopMgr Event Store Data Files Message Service Persistency Service H H H H H StoreGateSvc JobOptions Service Algorithm Algorithm T T T D D Particle Prop. Service Data Files D D StoreGateSvc D D Persistency Service D D D Detector Store Other Services Auditors Histogram Service Scripting Service

  4. Athena in Production • Data Challenge II: • phase 1: • ~30 Physics channels, 10s of millions of events • several million calibration events • currently producing raw data in a distributed worldwide environment using Grid • phase 2: reconstruction and real time distribution of data to tier 1 institutes • phase 3: worldwide distributed analysis on the Grid • Combined TestBeam • taking data since July in various configurations • ~5000 runs, > 1Tb data written • G4 simulation and reconstruction of CTB setup occurring in parallel • conditions databases in production, both read and write. • preparing for phase II: massive reconstruction of all real data and production of MC data.

  5. Combined Test Beam Setup

  6. Interval of Validity Service • Makes associations between user data and time dependent data that resides in specialized conditions databases • Transparent to user • Data only read from persistent layer when it is used. Validity interval information is separate from data • Hierarchical callback functions can be associated with time dependent data such that they are triggered when data enters a new interval of validity • Validity interval information and time dependent data can be preloaded on a job or run basis for trigger or testbeam situations where database access is unwanted

  7. Access to Time Varying Data • Maintains separation of transient and persistent layers • Testbeam environment making good use of IOVService for: • Slow control • Calibration

  8. Detector Pileup in DC2 • Overlay ~1000 min bias events to original physics stream • Requirement: digitization algorithms should run unchanged • Tuple event iterator: manage multiple input streams • Select random permutations from a circular buffer of min-bias events • Memory optimization: requirement total job size < 1GB 2-dim detector and time-dependent event caching • Stress test architecture flexibility • Excellent tool to expose memory leaks (they become x1000 bigger)

  9. History • Provenance of data must be assured • User selection of data based on its history. • full history of generation and processing recorded and associated with all data • Important in analysis to know complete source of data, and all cuts applied • History Service keeps track of • Environment • Job configuration • Services • Algorithms, AlgTools, SubAlgorithms • DataObjects

  10. Python Based Scripting Interface • Python woven into the framework, replacing flat text configuration files • dynamic job configuration • conditional branching • detFlags • interactive analysis • data object access and manipulation • connection to ROOT histogramming facilities • object type info for dictionaries and persistency

  11. Detector Configuration Matrix -+- 28 A DetFlags.Print() : pixel SCT TRT em HEC FCal Tile MDT CSC TGC RPC Truth LVL1 detdescr : ON ON ON -- -- -- ON -- -- -- -- ON -- digitize : ON ON ON -- -- -- ON -- -- -- -- ON -- geometry : ON ON ON -- -- -- ON -- -- -- -- ON -- haveRIO : -- -- -- -- -- -- -- -- -- -- -- -- -- makeRIO : -- -- -- -- -- -- -- -- -- -- -- -- -- pileup : ON ON ON -- -- -- -- -- -- -- -- ON -- readRDOBS : -- -- -- -- -- -- -- -- -- -- -- -- -- readRDOPool : ON ON ON -- -- -- ON -- -- -- -- ON -- readRIOBS : -- -- -- -- -- -- -- -- -- -- -- -- -- readRIOPool : -- -- -- -- -- -- -- -- -- -- -- -- -- simulate : -- -- -- -- -- -- -- -- -- -- -- -- -- writeBS : -- -- -- -- -- -- -- -- -- -- -- -- -- writeRDOPool : ON ON -- -- -- -- ON -- -- -- -- ON -- writeRIOPool : -- -- -- -- -- -- -- -- -- -- -- -- --

  12. Lessons Learned • Design for Performance • pileup excellent testbed • database access can be problematic • Design for Persistency • various container classes rewritten with persistency in mind • ClassID Service to globally monitor objects • Better support for realtime monitoring • essential for proper testbeam studies

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