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Grid Production Experience in the ATLAS Experiment. Kaushik De University of Texas at Arlington BNL Technology Meeting March 29, 2004. ATLAS Data Challenges. Original Goals (Nov 15, 2001) - PowerPoint PPT Presentation
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Grid Production Experience in the ATLAS Experiment
Kaushik DeKaushik DeUniversity of Texas at ArlingtonUniversity of Texas at Arlington
BNL Technology MeetingBNL Technology Meeting
March 29, 2004March 29, 2004
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 2
ATLAS Data Challenges
Original Goals (Nov 15, 2001)Original Goals (Nov 15, 2001) Test computing model, its software, its data model, and to ensure
the correctness of the technical choices to be made Data Challenges should be executed at the prototype Tier centres Data challenges will be used as input for a Computing Technical
Design Report due by the end of 2003 (?) and for preparing a MoU
Current StatusCurrent Status Goals are evolving as we gain experience Computing TDR ~end of 2004 DC’s are ~yearly sequence of increasing scale & complexity DC0 and DC1 (completed) DC2 (2004), DC3, and DC4 planned Grid deployment and testing is major part of DC’s
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 3
ATLAS DC1: July 2002-April 2003
Goals : Produce the data needed for the HLT TDR Get as many ATLAS institutes involved as possible
Worldwide collaborative activityParticipation : 56 Institutes
Australia Australia
AustriaAustria
CanadaCanada
CERN CERN
ChinaChina
Czech RepublicCzech Republic
Denmark Denmark **
France France
Germany Germany
GreeceGreece
IsraelIsrael
ItalyItaly
JapanJapan
Norway Norway **
PolandPoland
RussiaRussia
SpainSpain
Sweden Sweden **
TaiwanTaiwan
UKUK
USA USA ** * using Grid
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 4
(6300)(6300)(84)(84)2.5x102.5x1066ReconstructionReconstruction
+ Lvl1/2+ Lvl1/2
14141650165022224x104x1066Lumi02 Pile-upLumi02 Pile-up
22960096001251253x103x1077SimulationSimulation
Single part.Single part.
6060
2121
2323
TBTB
Volume of Volume of datadata
51000 (+6300)51000 (+6300)
37503750
60006000
3000030000
CPU-daysCPU-days
(400 SI2k)(400 SI2k)
kSI2k.monthskSI2k.months
4x104x1066
2.8x102.8x1066
101077
No. of No. of eventsevents
CPU TimeCPU TimeProcessProcess
690 (+84)690 (+84)TotalTotal
5050ReconstructionReconstruction
7878Lumi10 Pile-upLumi10 Pile-up
415415SimulationSimulation
Physics evt.Physics evt.
DC1 Statistics (G. Poulard, July 2003)
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 5
U.S. ATLAS DC1 Data Production
Year long process, Summer 2002-2003Year long process, Summer 2002-2003
Played 2nd largest role in ATLAS DC1Played 2nd largest role in ATLAS DC1
Exercised both farm and grid based productionExercised both farm and grid based production
10 U.S. sites participating10 U.S. sites participating Tier 1: BNL, Tier 2 prototypes: BU, IU/UC, Grid Testbed sites: ANL,
LBNL, UM, OU, SMU, UTA (UNM & UTPA will join for DC2)
Generated Generated ~2 million~2 million fully simulated, piled-up and fully simulated, piled-up and reconstructed eventsreconstructed events
U.S. was largest grid-based DC1 data producer in ATLASU.S. was largest grid-based DC1 data producer in ATLAS
Data used for HLT TDR, Athens physics workshop, Data used for HLT TDR, Athens physics workshop, reconstruction software tests...reconstruction software tests...
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 6
U.S. ATLAS Grid Testbed
BNL - U.S. Tier 1, 2000 nodes, 5% for BNL - U.S. Tier 1, 2000 nodes, 5% for ATLAS, 10 TB, HPSS through MagdaATLAS, 10 TB, HPSS through Magda
LBNL - pdsf cluster, 400 nodes, 5% for LBNL - pdsf cluster, 400 nodes, 5% for ATLAS (more if idle ~10-15% used), 1TBATLAS (more if idle ~10-15% used), 1TB
Boston U. - prototype Tier 2, 64 nodesBoston U. - prototype Tier 2, 64 nodes
Indiana U. - prototype Tier 2, 64 nodesIndiana U. - prototype Tier 2, 64 nodes
UT Arlington - new 200 cpu’s, 50 TBUT Arlington - new 200 cpu’s, 50 TB
Oklahoma U. - OSCER facilityOklahoma U. - OSCER facility
U. Michigan - test nodesU. Michigan - test nodes
ANL - test nodes, JAZZ clusterANL - test nodes, JAZZ cluster
SMU - 6 production nodesSMU - 6 production nodes
UNM - Los Lobos clusterUNM - Los Lobos cluster
U. Chicago - test nodesU. Chicago - test nodes
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 7
U.S. Production Summary
Number of Number of CPU hours CPU hours CPU hours
Files in Magda Events Simulation Pile-up Reconstruction
25 Gev di-jets 41k 1M ~60k 56k 60k+
50 Gev di-jets 10k 250k ~20k 22k 20k+
Single particles 24k 200k 17k 6k
Higgs sample 11k 50k 8k 2k
SUSY sample 7k 50k 13k 2k
minbias sample 7k ? ?
* Total ~30 CPU YEARS delivered to DC1 from U.S.* Total produced file size ~20TB on HPSS tape system, ~10TB on disk.* Black - majority grid produced, Blue - majority farm produced
Exercised both farm and grid based productionExercised both farm and grid based production Valuable large scale grid based production experienceValuable large scale grid based production experience
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 8
Grid Production Statistics
UTA33%
OU20%
LBL47%
Figure : Pie chart showing the sites where DC1 single particle simulation jobs were processed. Only three grid testbed sites were
used for this production in August 2002.
Figure : Pie chart showing the number of pile-up jobs successfully completed at various U.S. grid sites for dataset 2001 (25 GeV dijets). A total of 6000 partitions
were generated.
These are examples of some datasets produced on the Grid. Many other large samples were produced, especially at BNL using batch.
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 9
DC1 Production Systems
Local batch systems - bulk of productionLocal batch systems - bulk of production
GRAT - grid scripts, generated ~50k files produced in U.S.GRAT - grid scripts, generated ~50k files produced in U.S.
NorduGrid - grid system, ~10k files in Nordic countriesNorduGrid - grid system, ~10k files in Nordic countries
AtCom - GUI, ~10k files at CERN (mostly batch)AtCom - GUI, ~10k files at CERN (mostly batch)
GCE - Chimera based, ~1k files producedGCE - Chimera based, ~1k files produced
GRAPPA - interactive GUI for individual userGRAPPA - interactive GUI for individual user
EDG/LCG - test files onlyEDG/LCG - test files only
+ systems I forgot…+ systems I forgot…
More systems coming for DC2More systems coming for DC2 Windmill GANGA DIAL
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 10
GRAT Software
GRid Applications ToolkitGRid Applications Toolkit developed by KD, Horst Severini, Mark Sosebee, and students
Based on Globus, Magda & MySQLBased on Globus, Magda & MySQL
Shell & Python scripts, modular designShell & Python scripts, modular design
Rapid development platformRapid development platform Quickly develop packages as needed by DC
Physics simulation (GEANT/ATLSIM) Pileup production & data management Reconstruction
Test grid middleware, test grid performanceTest grid middleware, test grid performance
Modules can be easily enhanced or replaced, e.g. EDG Modules can be easily enhanced or replaced, e.g. EDG resource broker, Chimera, replica catalogue… (in progress)resource broker, Chimera, replica catalogue… (in progress)
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 11
GRAT Execution Model
1. Resource Discovery2. Partition Selection3. Job Creation4. Pre-staging5. Batch Submission6. Job Parameterization
DC1
Prod.(UTA)
RemoteGatekeeper
Replica(local)
MAGDA(BNL)
Param(CERN)
BatchExecution
scratch
1,4,5,10
2
3
4
5
6
7
89
7. Simulation8. Post-staging9. Cataloging10. Monitoring
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 12
Databases used in GRAT
Production databaseProduction database define logical job parameters & filenames track job status, updated periodically by scripts
Data management (Magda)Data management (Magda) file registration/catalogue grid based file transfers
Virtual Data CatalogueVirtual Data Catalogue simulation job definition job parameters, random numbers
Metadata catalogue (AMI)Metadata catalogue (AMI) post-production summary information data provenance
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 13
U.S. Middleware Evolution
Used for 95% of DC1 production
Used successfully for simulation
Tested for simulation, used forall grid-based reconstruction
Used successfully for simulation(complex pile-up workflow not yet)
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 14
DC1 Production Experience
Grid paradigm works, using GlobusGrid paradigm works, using Globus Opportunistic use of existing resources, run anywhere, from
anywhere, by anyone...
Successfully exercised grid middleware with increasingly Successfully exercised grid middleware with increasingly complex taskscomplex tasks Simulation: create physics data from pre-defined parameters and
input files, CPU intensive Pile-up: mix ~2500 min-bias data files into physics simulation files,
data intensive Reconstruction: data intensive, multiple passes Data tracking: multiple steps, one -> many -> many more mappings
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 15
Grid Quality of Service
Anything that can go wrong, WILL go wrongAnything that can go wrong, WILL go wrong During a 18 day run, every system died at least once Local experts were not always be accessible Examples: scheduling machines died 5 times (thrice power failure,
twice system hung), Network outages multiple times, Gatekeeper died at every site at least 2-3 times
All three databases died at least once! Scheduled maintenance - HPSS, Magda server, LBNL hardware... Poor cleanup, lack of fault tolerance in Globus
These outages should be expected on the grid - software These outages should be expected on the grid - software design must be robustdesign must be robust
We managed > 100 files/day (~80% efficiency) in spite of We managed > 100 files/day (~80% efficiency) in spite of these problems!these problems!
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 16
Software Issues
ATLAS software distribution worked well for DC1 farm ATLAS software distribution worked well for DC1 farm production, but not well suited for grid productionproduction, but not well suited for grid production
No integration of databases - caused many problemsNo integration of databases - caused many problems
Magda & AMI very useful - but we are missing data Magda & AMI very useful - but we are missing data management tool for truly distributed productionmanagement tool for truly distributed production
Required a lot of people to run production in the U.S., Required a lot of people to run production in the U.S., especially with so many sites on both grid and farmespecially with so many sites on both grid and farm
Startup of grid production slow - but learned useful lessonsStartup of grid production slow - but learned useful lessons
Software releases were often late - leading to chaotic last Software releases were often late - leading to chaotic last minute rush to finish productionminute rush to finish production
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 17
New Production System for DC2
GoalsGoals Automated data production
system for all ATLAS facilities Common database for all
production - Oracle currently Common supervisor run by all
facilities/managers - Windmill Common data management
system - Don Quichote Executors developed by
middleware experts (Capone, LCG, NorduGrid, batch systems, CanadaGrid...)
Final verification of data done by supervisor
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 18
Windmill - Supervisor
Supervisor development/U.S. DC production teamSupervisor development/U.S. DC production team UTA: Kaushik De, Mark Sosebee, Nurcan Ozturk + students BNL: Wensheng Deng, Rich Baker OU: Horst Severini ANL: Ed May
Windmill web pageWindmill web page http://www-hep.uta.edu/windmill
Windmill statusWindmill status version 0.5 released February 23
includes complete library of xml messages between agents includes sample executors for local, pbs and web services
can run on any Linux machine with Python 2.2 development continuing - Oracle production DB, DMS, new schema
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 19
Windmill Messaging
supervisoragent
executoragent
XML switch(JabberServer)
XMPP(XML)
XMPP(XML)
Webserver
SOAP
All messaging is XML based All messaging is XML based
Agents communicate using Jabber (open chat) protocolAgents communicate using Jabber (open chat) protocol
Agents have same command line interface - GUI in futureAgents have same command line interface - GUI in future
Agents & web server can run at same or different locationsAgents & web server can run at same or different locations
Executor accesses grid directly and/or thru web servicesExecutor accesses grid directly and/or thru web services
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 20
Intelligent Agents
Supervisor/executor are intelligent communication agentsSupervisor/executor are intelligent communication agents uses Jabber open source instant messaging framework Jabber server routes XMPP messages - acts as XML data switch reliable p2p asynchronous message delivery through firewalls built in support for dynamic ‘directory’, ‘discovery’, ‘presence’ extensible - we can add monitoring, debugging agents easily provides ‘chat’ capability for free - collaboration among operators Jabber grid proxy under development (LBNL - Agarwal)
JabberServer
Jabber Clients
Jabber Clients
XMPP
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 21
Core XML Messages
numJobsWantednumJobsWanted supervisor-executor negotiation of number of jobs to process
executeJobsexecuteJobs supervisor sends XML based job definitions
getExecutorDatagetExecutorData job acceptance, handle exchange (supports stateless executors)
getStatusgetStatus polling of job status
fixJobsfixJobs post-reprocessing and cleanup
killJobkillJob forced job abort
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 22
Core Windmill Libraries
interact.py - command line interface libraryinteract.py - command line interface library
agents.py - common intelligent agent libraryagents.py - common intelligent agent library
xmlkit.py - xml creation (generic) and parsing libraryxmlkit.py - xml creation (generic) and parsing library
messages.py - xml message creation (specific)messages.py - xml message creation (specific)
proddb.py - production database methods for oracle, mysql, proddb.py - production database methods for oracle, mysql, local, dummy, and possibly other optionslocal, dummy, and possibly other options
supervise.py - supervisor methods to drive productionsupervise.py - supervisor methods to drive production
execute.py - executor methods to run facilitiesexecute.py - executor methods to run facilities
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 23
Capone Executor
Various executors are being developedVarious executors are being developed Capone - U.S. VDT executor by U. of Chicago and Argonne Lexor - LCG executor mostly by Italian groups NorduGrid, batch (Munich), Canadian, Australian(?)
Capone is based on GCE (Grid Computing Environment)Capone is based on GCE (Grid Computing Environment) (VDT Client/Server, Chimera, Pegasus, Condor, Globus)
Status:Status: Python module Process “thread” for each job Archive of managed jobs Job management Grid monitoring
Aware of key parameters (e.g. available CPUs, jobs running)
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 24
Capone Architecture
Message interfaceMessage interface Web Service Jabber
Translation levelTranslation level Windmill
CPE (Capone Process Engine)CPE (Capone Process Engine)
Processes Processes Grid Stub DonQuixote
from Marco Mambellifrom Marco Mambelli
Message protocols
Translation
Web Service
CPE
Jabber
Windmill ADA
Stu
b
Grid
Don
Qu
ixote
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 25
Capone Processing
Supervisor requestsSupervisor requests
JobsJobs
Different job statuses:Different job statuses: 'received','translate','DAXgen','RLSreg','sch
eduling','cDAGgen','submission','running', 'checking','stageOut','cleaning','end','killing’
completed/failed (each step) Completion/failure (job)
DAXgen genmydax
recovery
RLSreg registerToRLS
recovery
translate gceTrans
recovery
received
recovery
running/checking check
recovery
stageOut stageOut
recovery
end fixJob
…
executeJob
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 26
Windmill Screenshots
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 27
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 28
Web Services Example
March 29, 2004March 29, 2004Kaushik De, BNL Technology MeetingKaushik De, BNL Technology Meeting 29
Conclusion
Data Challenges are important for ATLAS software and Data Challenges are important for ATLAS software and computing infrastructure readinesscomputing infrastructure readiness
Grids will be the default testbed for DC2Grids will be the default testbed for DC2
U.S. playing a major role in DC2 planning & productionU.S. playing a major role in DC2 planning & production
12 U.S. sites ready to participate in DC212 U.S. sites ready to participate in DC2
Major U.S. role in production software developmentMajor U.S. role in production software development
Test of new grid production system imminentTest of new grid production system imminent
Physics analysis will be emphasis of DC2 - new experiencePhysics analysis will be emphasis of DC2 - new experience
Stay tunedStay tuned