1. 1 ATLAS Grid Computing and Data Challenges Nurcan Ozturk University of Texas at Arlington Recent Progresses in High Energy Physics Bolu, Turkey. June 23-25, 2004

2. 2 Outline Introduction ATLAS Experiment ATLAS Computing System ATLAS Computing Timeline ATLAS Data Challenges DC2 Event Samples Data Production Scenario ATLAS New Production System Grid Flavors in Production System Windmill-Supervisor An Example of XML Messages Windmill-Capone Screenshots Grid Tools Conclusions

3. 3 Introduction Why Grid Computing: Scientific research becomes more and more complex and international teams of scientists grow larger and larger Grid technologies enables scientist to use remote computers and data storage systems to be able to retrieve and analyze the data around the world Grid Computing power will be a key to the success of the LHC experiments Grid computing is a challenge not only for particle physics experiments but also for biologists, astrophysicists and gravitational wave researchers

4. 4 ATLAS Experiment ATLAS (A Toroidal LHC Apparatus) experiment at the Large Hadron Collider at CERN will start taking data in 2007. proton-proton collisions with a 14 TeV center-of-mass energy ATLAS will study: SM Higgs Boson SUSY states SM QCD, EW, HQ Physics New Physics? Total amount of “raw” data:  1 PB/year Needs the GRID to reconstruct and analyze this data: Complex “Worldwide Computing Model” and “Event Data Model” Raw Data @ CERN Reconstructed data “distributed” All members of the collaboration must have access to “ALL” public copies of the data ~2000 Collaborators ~150 Institutes 34 Countries

5. 5 Tier2 Centre ~200kSI2k Event Builder Event Filter ~159kSI2k T0 ~5MSI2k UK Regional Centre (RAL) US Regional Centre French Regional Centre Italian Regional Centre SheffieldManchest er Liverpool Lancaster ~0.25TIPS Workstations 10 GB/sec 450 Mb/sec 100 - 1000 MB/s Some data for calibration and monitoring to institutes Calibrations flow back Each Tier 2 has ~25 physicists working on one or more channels Each Tier 2 should have the full AOD, TAG & relevant Physics Group summary data Tier 2 do bulk of simulation Physics data cache ~Pb/sec ~ 300MB/s/T1 /expt Tier2 Centre ~200kSI2k  622Mb/s Tier 0 Tier 1 Desktop PC (2004) = ~1 kSpecInt2k Northern Tier ~200kSI2k Tier 2  ~200 Tb/year/T2  ~7.7MSI2k/T1  ~2 Pb/year/T1  ~9 Pb/year/T1  No simulation  622Mb/s ATLAS Computing System (R. Jones)

6. 6 POOL/SEAL release (done) ATLAS release 7 (with POOL persistency) (done) LCG-1 deployment (done) ATLAS complete Geant4 validation (done) ATLAS release 8 (done) DC2 Phase 1: simulation production DC2 Phase 2: intensive reconstruction (the real challenge!) Combined test beams (barrel wedge) Computing Model paper Computing Memorandum of Understanding ATLAS Computing TDR and LCG TDR DC3: produce data for PRR and test LCG-n Physics Readiness Report Start commissioning run GO! 2003 2004 2005 2006 2007 NOW ATLAS Computing Timeline (D. Barberis)

7. 7 ATLAS Data Challenges Data Challenges --> generate and analyze simulated data with increasing scale and complexity using Grid (as much as possible) Goal: Validation of the Computing Model, the software, the data model, and to ensure the correctness of the technical choices to be made Provide simulated data to design and optimize the detector Experience gained these Data Challenges will be used to formulate the ATLAS Computing Technical Design Report Status: DC0 (December2001-June2002), DC1 (July2002-March2003) completed DC2 ongoing DC3, DC4 planned (one/year)

8. 8 DC2 Event Samples (G. Poulard)

9. 9 Data Production Scenario (G. Poulard)

10. 10 ATLAS New Production System LCGNGGrid3LSF LCG exe LCG exe NG exe G3 exe LSF exe super prodDB dms RLS jabber soap jabber Don Quijote Windmill Lexor AMI Capone Dulcinea http://www.nordugrid.org/applications/prodsys/

11. 11 Grids Flavors in Production System LCG: LHC Computing Grid, > 40 sites Grid3: USA Grid, 27 sites NorduGrid: Denmark, Sweden, Norway, Finland, Germany, Estonia, Slovenia, Slovakia, Australia, Switzerland, 35 sites L. Perini

12. 12 Windmill-Supervisor Supervisor development team at UTA: Kaushik De, Nurcan Ozturk, Mark Sosebee supervisor-executor communication is via Jabber protocol developed for Instant Messaging XML (Extensible Markup Language ) messages are passed between supervisor- executor supervisor-executor interaction: numJobsWanted executeJobs getExecutorData getStatus fixJob killJob Final verification of jobs is done by supervisor Windmill webpage: http://www-hep.uta.edu

13. 13 An Example of XML Messages JobTransforms-8.0.1.2 Atlas-8.0.1 – software version 100000 - minimum CPU required for a production job specint2000seconds - unit of CPU usage 500 - maximum output file size MB no - IP connection required from CE 256 - minimum physical memory requirement MB 5 100000 specint2000 numJobWanted : supervisor-executor negotiation of number of jobs to process supervisor’s request executor’s respond

14. 14 Windmill-Capone Screenshots

15. 15 Grid Tools An example: Grid3 - USA Grid Joint project with USATLAS, USCMS, iVDGL, PPDG, GriPhyN Components: VDT based Classic SE (gridftp) Monitoring: Grid site Catalog, Ganglia, MonALISA Two RLS servers and VOMS server for ATLAS Installation: pacman –get iVDGL:Grid3 Takes ~ 4 hours to bring up a site from scratch VDT (Virtual Data Toolkit) version 1.1.14 gives: Virtual Data System 1.2.3 Class Ads 0.9.5 Condor 6.6.1 EDG CRL Update 1.2.5 EDG Make Gridmap 2.1.0 Fault Tolerant Shell (ftsh) 2.0.0 Globus 2.4.3 plus patches GLUE Information providers GLUE Schema 1.1, extended version 1 GPT 3.1 GSI-Enabled OpenSSH 3.0 Java SDK 1.4.1 KX509 2031111 Monalisa 0.95 MyProxy 1.11 Netlogger 2.2 PyGlobus 1.0 PyGlobus URL Copy 1.1.2.11 RLS 2.1.4 UberFTP 1.3 What tools are needed for a Grid site?

16. 16 Conclusions Grid paradigm works; opportunistic use of existing resources, run anywhere, from anywhere, by anyone... Grid computing is a challenge, needs world wide collaboration Data production using Grid is possible, successful so far Data Challenges are the way to test the ATLAS computing model before the real experiment starts Data Challenges also provides data for Physics groups A learning and improving experience with Data Challenges