1. INFSO-RI-508833 Enabling Grids for E-sciencE http://arda.cern.ch LCG ARDA project Status and plans Dietrich Liko / CERN

2. 2 The ARDA project ARDA is an LCG project –main activity is to enable LHC analysis on the grid –ARDA is contributing to EGEE Includes entire CERN NA4-HEP resource (NA4 = Applications) Interface with the new EGEE middleware (gLite) –By construction, ARDA uses the new middleware Follow the grid software as it matures –Verify the components in an analysis environments Contribution in the experiments framework (discussion, direct contribution, benchmarking,…) Users feedback is fundamental – in particular physicists needing distributed computing to perform their analyses –Provide early and continuous feedback

3. 3 ARDA prototype overview LHC Experiment Main focusBasic prototype component /framework Middleware GUI to GridGANGA/DaVinci Interactive analysis PROOF/AliROOT High-level services DIAL/Athena Explore/exploit native gLite functionality ORCA

4. 4 Ganga4 Major version Important contribution from the ARDA team Interesting concepts Note that GANGA is a joint ATLAS-LHCb project Contacts with CMS (exchange of ideas, code snippets, …) You have seen already everything in the presentation of Ulrik

5. 5 ALICE prototype ROOT and PROOF ALICE provides –the UI –the analysis application (AliROOT) GRID middleware gLite provides all the rest ARDA/ALICE is evolving the ALICE analysis system UI shell Application Middleware end to end

6. 6 USER SESSION PROOF PROOF SLAVES PROOF MASTER SERVER PROOF SLAVES Site A Site C Site B PROOF SLAVES Demo based on a hybrid system using 2004 prototype Demo at Supercomputing 04 and Den Haag

7. 7 ARDA shell + C/C++ API C++ access library for gLite has been developed by ARDA High performance Protocol quite proprietary... Essential for the ALICE prototype Generic enough for general use Using this API grid commands have been added seamlessly to the standard shell

8. 8 Current Status Developed gLite C++ API and API Service –providing generic interface to any GRID service C++ API is integrated into ROOT –In the ROOT CVS –job submission and job status query for batch analysis can be done from inside ROOT Bash interface for gLite commands with catalogue expansion is developed –More powerful than the original shell –In use in ALICE –Considered a generic mw contribution (essential for ALICE, interesting in general) First version of the interactive analysis prototype ready Batch analysis model is improved –submission and status query are integrated into ROOT –job splitting based on XML query files –application (Aliroot) reads file using xrootd without prestaging

9. 9 ATLAS/ARDA Main component: –Contribute to the DIAL evolution gLite analysis server Embedded in the experiment –AMI tests and interaction –Production and CTB tools –Job submission (ATHENA jobs) –Integration of the gLite Data Management within Don Quijote –Active participation in several ATLAS reviews – Benefit from the other experiments prototypes –First look on interactivity/resiliency issues e.g. use of DIANE –GANGA (Principal component of the LHCb prototype, key component of the overall ATLAS strategy) Tao-Sheng Chen, ASCC

10. 10 SE Nordugrid DQ server RLS DQ Client DQ server RLS SE RLS gLiteLCG GRID3 Data Management Don Quijote Locate and move data over grid boundaries ARDA has connected gLite

11. 11 Combined Test Beam Example: ATLAS TRT data analysis done by PNPI St Petersburg Number of straw hits per layer Real data processed at gLite Standard Athena for testbeam Data from CASTOR Processed on gLite worker node

12. 12 DIANE

13. 13 DIANE on gLite running Athena

14. 14 DIANE on LCG (Taiwan) A worker died – no problem, its tasks get reallocated Job need some time to start up. No problem.

15. 15 Prototype (ASAP) Contributions to CMS-specific components –RefDB/PubDB Usage of components used by CMS –Notably Monalisa Contribution to CMS-specific developments –Physh ARDA/CMS

16. 16 RefDB Re-Design and PubDB –Taking part in the RefDB redesign –Developing schema for PubDB and supervising development of the first PubDB version Analysis Prototype Connected to MonAlisa –To track the progress of an analysis task is troublesome when the task is split into several (hundreds of) sub-jobs –Analysis prototype associates each sub-job with built-in identity and capability to report its progress to the MonAlisa system –MonAlisa service receives and combines progress reports of single sub-jobs and publishes the overall progress of the whole task ARDA/CMS

17. 17 CMS - Using MonAlisa for user job monitoring A single job Is submiited to gLite JDL contains job-splitting instructions Master job is split by gLite into sub-jobs Dynamic monitoring of the total number of the events of processed by all sub-jobs belonging to the same Master job Demo at Supercomputing 04

18. 18 ARDA/CMS PhySh –Physicist Shell –ASAP is Python-based and it uses XML-RPC calls for client-server interaction like Clarens and PhySh

19. 19 ARDA/CMS CMS prototype (ASAP = Arda Support for cms Analysis Processing) –First version of the CMS analysis prototype capable of creating- submitting-monitoring of the CMS analysis jobs on the gLite middleware had been developed by the end of the year 2004 Demonstrated at the CMS week in December 2004 –Prototype was evolved to support both RB versions deployed at the CERN testbed (prototype task queue and gLite 1.0 WMS ) –Currently submission to both RBs is available and completely transparent for the users (same configuration file, same functionality) –Plan to implement gLite job submission handler for Crab

20. 20 ASAP: Starting point for users The user is familiar with the experiment application needed to perform the analysis (ORCA application for CMS) –The user knows how to create executable able to run the analysis task (reading selected data samples, use the data to compute derived quantities, take decisions, fill histograms, select events, etc…). The executable is based on the experiment framework The user debugged the executable on small data samples, on a local computer or computing services (e.g. lxplus at CERN) How to go for larger samples, which can be located at any regional center CMS-wide? The users should not be forced : –to change anything in the compiled code –to change anything in the configuration file for ORCA –to know where the data samples are located

21. 21 ASAP work and information flow RefDBPubDB ASAP UI Monalisa gLite JDL Job monitoring directory ASAP Job Monitoring service Publishing Job status On the WEB Delegates user credentials using MyProxy Job submission Checking job status Resubmission in case of failure Fetching results Storing results to Castor Output files location Application,applicationversion, Executable, Orca data cards Data sample, Working directory, Castor directory to save output, Number of events to be processed Number of events per job Job running on the Worker Node

22. 22 Job Monitoring ASAP Monitor

23. 23 Merging the results

24. 24 Bkg. samples Processed with Br, mb Kine presel. qcd, p T = 50-80 GeV/c 100K Arda 2.08 x 10 -2 2.44 x 10 -4 qcd, p T = 80-120 GeV/c200K crab 2.94 x 10 -3 5.77 x 10 -3 qcd, p T = 120-170 GeV/c200K Arda 5.03 x 10 -4 4.19 x 10 -2 qcd, p T > 170 GeV/c 1M 1.33 x 10 -4 2.12 x 10 -1 tt, W-> 80K crab 5.76 x 10 -9 4.88 x 10 -2 Wt, W-> 30K Arda 7.10 x 10 -10 1.38 x 10 -2 W+j, W-> 400K crab 5.74 x 10 -7 2.16 x 10 -2 Z/ *-> 130<m < 300 GeV/c 2 70K Arda 1.24 x 10 -8 9.53 x 10 -2 Z/ *-> m > 300 GeV/c 2 60K gross 6.22 x 10 -10 3.23 x 10 -1 H->2 ->2j analysis : bkg. data available (all signal events processed with Arda) A. Nikitenko (CMS)

25. 25 Higgs boson mass (M ) reconstruction (M H ) ~ (E T miss ) / sin( j1j2 ) Higgs boson mass was reconstructed after basic off-line cuts: reco E T jet > 60 GeV, E T miss > 40 GeV. M evaluation is shown for the consecutive cuts : p > 0 GeV/c, p > 0 GeV/c, j1j2 < 175 0. M and (M ) are in a very good agreement with old results CMS Note 2001/040, Table 3: M = 455 GeV/c 2, (M )=77 GeV/c 2. ORCA4, Spring 2000 production. A. Nikitenko (CMS)

26. 26 ARDA ASAP First users were able to process their data on gLite –Work of these pilot users can be regarded as a first round of validation of the gLite middleware and analysis prototypes The number of users should increase as soon as preproduction system will become available –Interest to have CPUs at the centres where data sits (LHC Tier-1s) To enable user analysis on the Grid: –we will continue to work in the close collaboration with the physics community and gLite developers ensuring good level of communication between them providing constant feedback to the gLite development team Key factors to progress: –Increasing number of users –Larger distributed systems –More middleware components

27. 27 ARDA Feedback (gLite middleware) 2004: –Prototype available (CERN + Madison Wisconsin) –A lot of activity (4 experiments prototypes) –Main limitation: size Experiments data available! Just an handful of worker nodes 2005: –Coherent move to prepare a gLite package to be deployed on the pre-production service ARDA contribution: Mentoring and tutorial Actual tests! –Lot of testing during 05Q1 –PreProduction Service is about to start! Access granted on May 18 th 2004!

28. 28 WMS monitor –Hello World! jobs –1 per minute since last Febraury –Logging&Bookkeeping info on the web to help the developers

29. 29 Data Management Central component together with the WMS Early tests started in 2004 Two main components: –gLiteIO (protocol + server to access the data) –FiReMan (file catalogue) –The two components are not isolated, for example gLiteIO uses the ACL as recorded in FiReMan, FiReMan exposes the physical location of files for the WMS to optimise the job submissions… Both LFC and FiReMan offer large improvements over RLS –LFC is the most recent LCG2 catalogue Still some issues remaining: –Scalability of FiReMan –Bulk Entry for LFC missing –More work needed to understand performance and bottlenecks –Need to test some real Use Cases –In general, the validation of DM tools takes time!

30. 30 FiReMan Performance - Queries Query Rate for an LFN 0 200 400 600 800 1000 1200 5 10 15 20 25 30 35 40 45 50 Entries Returned / Second Number Of Threads Fireman Single Fireman Bulk 1 Fireman Bulk 10 Fireman Bulk 100 Fireman Bulk 500 Fireman Bulk 1000 Fireman Bulk 5000

31. 31 FiReMan Performance - Queries Comparison with LFC: 0 200 400 600 800 1000 1200 1 2 5 10 20 50 100 Entries Returned / Second Number Of Threads Fireman - Single Entry Fireman - Bulk 100 LFC

32. 32 More data coming… C. Munro (ARDA & Brunel Univ.) at ACAT 05

33. 33 Summary of gLite usage and testing Info available also under http://lcg.web.cern.ch/lcg/PEB/arda/LCG_ARDA_Glite.htm http://lcg.web.cern.ch/lcg/PEB/arda/LCG_ARDA_Glite.htm gLite version 1 WMS Continuous monitor available on the web (active since 17 th of February) Concurrency tests Usage with ATLAS and CMS jobs (Using Storage Index) Good improvements observed DMS (FiReMan + gLiteIO) Early usage and feedback (since Nov04) on functionality, performance and usability Considerable improvement in performances/stability observed since Some of the tests given to the development team for tuning and to JRA1 to be used in the testing suite Most of the tests given to JRA1 to be used in the testing suite Performance/stability measurements: heavy-duty testing needed for real validation Contribution to the common testing effort to finalise gLite 1 with SA1, JRA1 and NA4-testing) Migration of certification tests within the certification test suite (LCG gLite) Comparison between LFC (LCG) and FiReMan Mini tutorial to facilitate the usage of gLite within the NA4 testing

34. 34 Metadata services on the Grid gLite has provided a prototype for the EGEE Biomed community (in 2004) Requirements in ARDA (HEP) were not all satisfied by that early version ARDA preparatory work –Stress testing of the existing experiment metadata catalogues –Existing implementations showed to share similar problems ARDA technology investigation –On the other hand usage of extended file attributes in modern systems (NTFS, NFS, EXT2/3 SCL3,ReiserFS,JFS,XFS) was analysed: a sound POSIX standard exists! Prototype activity in ARDA Discussion in LCG and EGEE and UK GridPP Metadata group Synthesis: –New interface which will be maintained by EGEE benefiting from the activity in ARDA (tests and benchmarking of different data bases and direct collaboration with LHCb/GridPP)

35. 35 ARDA Implementation Prototype –Validate our ideas and expose a concrete example to interested parties Multiple back ends –Currently: Oracle, PostgreSQL, SQLite Dual front ends –TCP Streaming Chosen for performance –SOAP Formal requirement of EGEE Compare SOAP with TCP Streaming Also implemented as standalone Python library –Data stored on the file system

36. 36 Dual Front End Text based protocol Data streamed to client in single connection Implementations –Server – C++, multiprocess –Clients – C++, Java, Python, Perl, Ruby Most operations are SOAP calls Based on iterators –Session created –Return initial chunk of data and session token –Subsequent request: client calls nextQuery() using session token –Session closed when: End of data Client calls endQuery() Client timeout Implementations –Server – gSOAP (C++). –Clients – Tested WSDL with gSOAP, ZSI (Python), AXIS (Java) Clean way to study performance implications of protocols…

37. 37 More data coming… N. Santos (ARDA & Coimbra Univ.) at ACAT 05 Test protocol performance –No work done on the backend –Switched 100Mbits LAN Language comparison –TCP-S with similar performance in all languages –SOAP performance varies strongly with toolkit Protocols comparison –Keepalive improves performance significantly –On Java and Python, SOAP is several times slower than TCP-S Measure scalability of protocols –Switched 100Mbits LAN TCP-S 3x faster than gSoap (with keepalive) Poor performance without keepalive –Around 1.000 ops/sec (both gSOAP and TCP-S) 1000 pings

38. 38 Current Uses of the ARDA Metadata prototype Evaluated by LHCb bookkeeping –Migrated bookkeeping metadata to ARDA prototype 20M entries, 15 GB –Feedback valuable in improving interface and fixing bugs –Interface found to be complete –ARDA prototype showing good scalability Ganga (LHCb, ATLAS) –User analysis job management system –Stores job status on ARDA prototype –Highly dynamic metadata Discussed within the community –EGEE –UK GridPP Metadata group

39. 39 ARDA workshops and related activities ARDA workshop (January 2004 at CERN; open) ARDA workshop (June 21-23 at CERN; by invitation) –The first 30 days of EGEE middleware NA4 meeting (15 July 2004 in Catania; EGEE open event) ARDA workshop (October 20-22 at CERN; open) –LCG ARDA Prototypes –Joint session with OSG NA4 meeting 24 November (EGEE conference in Den Haag) ARDA workshop (March 7-8 2005 at CERN; open) ARDA workshop (October 2005; together with LCG Service Challenges) Wednesday afternoon meeting started in 2005: –Presentations from experts and discussion (not necessary from ARDA people) Available from http://arda.cern.chhttp://arda.cern.ch

40. 40 Conclusions (1/3) ARDA has been set up to –Enable distributed HEP analysis on gLite Contact have been established With the experiments With the middleware developers Experiment activities are progressing rapidly –Prototypes for ALICE, ATLAS, CMS & LHCb Complementary aspects are studied Good interaction with the experiments environment –Always seeking for users!!! People more interested in physics than in middleware… we support them! –2005 will be the key year (gLite version 1 is becoming available on the pre- production service)

41. 41 Conclusions (2/3) ARDA provides special feedback to the development team –First use of components (e.g. gLite prototype activity) –Try to run real-life HEP applications –Dedicated studies offer complementary information Experiment-related ARDA activities produce elements of general use –Very important by-product –Examples: Shell access (originally developed in ALICE/ARDA) Metadata catalog (proposed and under test in LHCb/ARDA) (Pseudo)-interactivity experience (something in/from all experiments )

42. 42 Conclusions (3/3) ARDA is a privileged observatory to follow, contribute and influence the evolution of the HEP analysis –Analysis prototypes are a good idea! Technically, they complement the data challenges experience Key point: these systems are exposed to users –The approach of 4 parallel lines is not too inefficient Contributions in the experiments from day zero Difficult environment Commonality can not be imposed… –We could do better in keeping good connection with OSG How?

43. 43 Outlook Commonality is a very tempting concept, indeed… –Sometimes a bit fuzzy, maybe… Maybe it is becoming more important … –Lot of experience in the whole community! –Baseline services ideas –LHC schedule: physics is coming! Maybe it is emerging… (examples are not exhaustive) –Interactivity is a genuine requirement: e.g. PROOF and DIANE –Toolkits for the users to build applications on top of the computing infrastructure: e.g. GANGA –Metadata/workflow systems open to the users –Monitor and discovery services open to users: e.g. Monalisa in ASAP Strong preference for a a posteriori approach –All experiments still need their system… –Keep on being pragmatic …

44. 44 People Massimo Lamanna Frank Harris (EGEE NA4) Birger Koblitz Andrey Demichev Viktor Pose Victor Galaktionov Derek Feichtinger Andreas Peters Hurng-Chun Lee Dietrich Liko Frederik Orellana Tao-Sheng Chen Julia Andreeva Juha Herrala Alex Berejnoi Andrew Maier Kuba Moscicki Wei-Long Ueng 2 PhD students: Craig Munro (Brunel Univ.) Distributed analysis within CMS working mainly with Julia Nuno Santos (Coimbra Univ) Metadata and resilient computing working mainly with Birger Catalin Cirstoiu and Slawomir Biegluk (short-term LCG visitors) LHCb CMS ATLAS ALICE Good collaboration with EGEE/LCG Russian institutes and with ASCC Taipei