Alexei Klimentov Brookhaven National Laboratory

Alexei Klimentov : ATLAS Distributed Computing

NEC2009 Varna- 10 September 2009

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Alexei Klimentov

Brookhaven National Laboratory

ATLAS Distributed ComputingComputing Model, Data Management, Production System, Distributed Analysis, Information System, Monitoring



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Introduction

The title that Vladimir gave me cannot be done in

20 mins.

I’ll talk about Distributed Computing

Components, but I am certainly biased as any

Operations person.



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ATLAS Collaboration

6 Continents 37 Countries 169 Institutions 2800 Physicists 700 Students>1000 Technical and support staff

Albany, Alberta, NIKHEF Amsterdam, Ankara, LAPP Annecy, Argonne NL, Arizona, UT Arlington, Athens, NTU Athens, Baku,

IFAE Barcelona, Belgrade, Bergen, Berkeley LBL and UC, HU Berlin, Bern, Birmingham, Bogotá, Bologna, Bonn, Boston, Brandeis,

Bratislava/SAS Kosice, Brookhaven NL, Buenos Aires, Bucharest, Cambridge, Carleton, Casablanca/Rabat, CERN, Chinese Cluster, Chicago,

Chilean Cluster (Santiago+Valparaiso), Clermont-Ferrand, Columbia, NBI Copenhagen, Cosenza, AGH UST Cracow, IFJ PAN Cracow, DESY,

Dortmund, TU Dresden, JINR Dubna, Duke, Frascati, Freiburg, Geneva, Genoa, Giessen, Glasgow, Göttingen, LPSC Grenoble, Technion Haifa,

Hampton, Harvard, Heidelberg, Hiroshima, Hiroshima IT, Indiana, Innsbruck, Iowa SU, Irvine UC, Istanbul Bogazici, KEK, Kobe, Kyoto, Kyoto UE,

Lancaster, UN La Plata, Lecce, Lisbon LIP, Liverpool, Ljubljana, QMW London, RHBNC London, UC London, Lund, UA Madrid, Mainz, Manchester,

Mannheim, CPPM Marseille, Massachusetts, MIT, Melbourne, Michigan, Michigan SU, Milano, Minsk NAS, Minsk NCPHEP, Montreal,

McGill Montreal, FIAN Moscow, ITEP Moscow, MEPhI Moscow, MSU Moscow, Munich LMU, MPI Munich, Nagasaki IAS, Nagoya, Naples,

New Mexico, New York, Nijmegen, BINP Novosibirsk, Ohio SU, Okayama, Oklahoma, Oklahoma SU, Oregon, LAL Orsay, Osaka, Oslo, Oxford,

Paris VI and VII, Pavia, Pennsylvania, Pisa, Pittsburgh, CAS Prague, CU Prague, TU Prague, IHEP Protvino, Regina, Ritsumeikan,

UFRJ Rio de Janeiro, Rome I, Rome II, Rome III, Rutherford Appleton Laboratory, DAPNIA Saclay, Santa Cruz UC, Sheffield, Shinshu, Siegen, Simon

Fraser Burnaby, SLAC, Southern Methodist Dallas, PNPI St.Petersburg, Stockholm, KTH Stockholm, Stony Brook, Sydney, AS Taipei, Tbilisi,

Tel Aviv, Thessaloniki, Tokyo ICEPP, Tokyo MU, Toronto, TRIUMF, Tsukuba, Tufts, Udine/ICTP, Uppsala, Urbana UI, Valencia,

UBC Vancouver, Victoria, Washington, Weizmann Rehovot, FH Wiener Neustadt, Wisconsin, Wuppertal, Yale, Yerevan



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Necessity of Distributed Computing?

ATLAS will collect RAW data at 320 MB/s for 50k seconds/day and ~100

days/year

RAW data: 1.6 PB/year

Processing (and re-processing) these events will require ~10k CPUs full time

the first year of data-taking, and a lot more in the future as data accumulate

Reconstructed events will also be large, as people want to study detector

performance as well as do physics analysis using the output data

ESD data: 1.0 PB/year, AOD data: 0.1 PB/year

At least 10k CPUs are also needed for continuous simulation production of at

least 30% of the real data rate and for analysis

There is no way to concentrate all needed computing power and storage

capacity at CERN

The LEP model will not scale to this level

The idea of distributed computing, and later of the computing grid, became

fashionable at the turn of the century and looked promising when applied to

HEP experiments’ computing needs



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Copy RAW data to CERN Castor Mass Storage System tape for archival

Copy RAW data to Tier-1s for storage and reprocessing

Run first-pass calibration/alignment (within 24 hrs)

Run first-pass reconstruction (within 48 hrs)

Distribute reconstruction output (ESDs, AODs & TAGs) to Tier-1s

5 Calibration

Tier 2

5 sites in Europe and US

RAW

Archive a fraction of RAW data

(Re)run calibration and alignment

Re-process data with better calib/align or/and algo

Distribute derived data to Tier-2sRun HITS reconstruction and large-scale event selection and analysis jobs

Computing Model : Main Operations

Tier 3Contribute to MC simulation

Users AnalysisO(100) sites Worldwide

AODTAG

Incomplete list of Data Formats:ESD : Event Summary DataAOD : Analysis Object DataDPD : Derived Physics DataTAG : event meta-information

TAGRun MC simulation

Keep AOD and TAG for the analysis

Run analysis jobs(36 Tier-2s, ~80 sites)



Tier-0

BNL

IN2P3

FZK

IN2P3 MoU & CMRAW, ESD 15%, AOD,DPD,TAG 100%

FZK MoU and CMRAW, ESD 10%, AOD,DPD,TAG 100%

ASGCASGCASGCASGCTier-1

MWT2

AGLT2

NET2

SWT2

SLAC

ATLAS Grid Sites and Data Distribution

3 Grids, 10 Tier-1s, ~80 Tier-2(3)sTier-1 and associated Tier-ns form cloud. ATLAS clouds have from 2 to 15 sites. We also have T1-T1 associations.

Input Rates Estimation (Tier-1s)

BNL MoU & CMRAW 24%, ESD, AOD,DPD,TAG 100%

Data export from CERNreProcessed and MC data distribution

Tier-1 BNL CNAF FZK IN2P3 NDGF PIC RAL SARA TAIWAN TRIUMF

Summary

Tape (MB/s)

80 16 32 48 16 16 32 48 16 16 320

Disk (MB/s)

240 60 80 100 60 60 80 100 60 60 800

Total (MB/s)

320 76 112 148 76 76 112 148 76 76 1220

ATLAS Tier-1s Data Shares

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Ubiquitous

Wide Area Network

Bandwidth

First Computing TDR’s assumed not enough network bandwidth

The Monarch project proposed multi Tier model with this in mind

Today network bandwidth is our least problem

But we still have the Tier model in the LHC experiments

Not in all parts of the world ideal network yet (last mile)

LHCOPN provides excellent backbone for Tier-0 and Tier-1’s

Each LHC experiment has adopted differently

K.Bos. “Status and Prospects of The LHC Experiments Computing”. CHEP’09



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Distributed Computing Components

The ATLAS Grid architecture is based on : Distributed Data Management (DDM)

Distributed Production System (ProdSys, PanDA)

Distributed Analysis (DA), GANGA, PanDA

Monitoring

Grid Information System

Accounting

Networking

Databases



ATLAS Distributed Data Management.

1/2

The second generation of ATLAS DDM system (DQ2) DQ2 is built on top of Grid data transfer tools

Moved to dataset based approach Datasets : an aggregation of files plus associated DDM metadata

Datasets is a unit of storage and replication

Automatic data transfer mechanisms using distributed site services

Subscription system

Notification system Technicalities :

Global services dataset repository dataset location catalog logical file names only, no global physical file catalog

Local Site services (LocalFileCatalog) It provides logical to physical file name mapping.


NEC2009 Varna- 10 September 2009ATLAS Distributed Data Management. 2/2

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STEP09STEP09Data export from CERN to Tiersday/averageMB/s

Reprocessed datasets replication between Tier-1s (ΔΤ [hours] = T_last_file_transfer – T_subscription)

99% of data were transferredwithin 4 hours

Latency in reprocessingor site issue

One dataset wasn’t replicated after 3 days

Days of running



ATLAS Production System 1/2

Manages ATLAS simulation (full chain) and reprocessing jobs on the wLCG Task request interface to define a related group of jobs

Input : DQ2 dataset(s) (with the exception of some event generation)

Output : DQ2 dataset(s) (the jobs are done only when the output is at the Tier-1)

Due to temporary site problems, jobs are allowed several attempts

Job definition and attempt state are stored in Production Database (Oracle DB)

Jobs are supervised by ATLAS Production System

Consists of many components DDM/DQ2 for data management

PanDA task request interface and job definitions

PanDA for job supervision

ATLAS Dashboard and PanDA monitor for monitoring

Grid Middlewares

ATLAS software

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ATLAS Production System 2/2

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Task request interfaceProduction Database: job definition, job states, metadata

Tasks Input: DQ2 datasetsTask states

Tasks Output: DQ2 datasets

3 Grids/10 Clouds/90+Production Sites

Monitor sites, tasks, jobs

Job brokering is done by the PanDA Service (bamboo) according to input data and site availability

A.Read, Mar09



Data Processing Cycle

Data processing at CERN (Tier-0 processing) First-pass processing of the primary event stream

The derived datasets (ESD, AOD, DPD, TAG) are distributed from the Tier-0 to the Tier-1s

RAW data (received from Event Filter Farm) are exported within 24h. This is why first-pass processing can be done by Tier-1s (though this facility was not used during LHC beam and cosmic ray runs)

Data reprocessing at Tier-1s 10 Tier-1 centers world wide. Each takes a subset of RAW data

(Tier-1 shares from 5% to 25%), ATLAS production facilities at CERN can be used in case of emergency.

Each Tier-1 reprocessed its share of RAW data. The derived datasets are distributed ATLAS-wide.

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See P.Nevski’ talk NEC2009, LHC Computing


NEC2009 Varna- 10 September 2009ATLAS Data Simulation and

Reprocessing

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RunningJobs

ReprocessingSep08-Sep09

Production System in continuous operations10 clouds use LFC as file catalog and Panda as jobs executorCPUs are under utilized in average, peak rate 33kjobs/dayProdSys can produce 100 TB/week of MCAverage walltime efficiency is over 90%System does : Data simulation and data reprocessing



ATLAS Distributed Analysis

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Probably the most important area at this pointIt depends on a functional data management and job management system

Two widely used distributed analysis tools (Ganga and pathena)They capture the great majority of usersWe expect the usage to grow substantially in the preparation and especially in the 2009/10 run Present/traditional use cases: AOD/DPD analysis clearly very important But also run over selected RAW (for detector debugging, studying etc…)

J.Elmsheuser Sep09ATLAS jobs go to the data


NEC2009 Varna- 10 September 2009ATLAS Grid Information System (AGIS)

The overall purpose of ATLAS Grid Information

System is to store and to expose static, dynamic

and configuration parameters needed by ATLAS

Distributed Computing (ADC) applications. AGIS is

a database oriented system.

The first AGIS proposal from G.Poulard. The pioneering work of

R.Pezoa and R.Rocha in summer 2008, and definition of basic

design principles implemented in ‘dashboards’. Now development

is leaded by ATLAS BINP group.

Today’s situation :

various configuration parameters and information about available resources, services and its status and properties are extracted from different sources or they are defined in different configuration files (sometimes Grid information is hard coded in application programs).

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AGIS Architecture Overview

System architecture should allow to add new classes of

information or sites configuration parameters, to

reconfigure ATLAS clouds topology and production

queues, to add and to modify users information.

AGIS is ORACLE based information system.

AGIS stores as read-only data extracted from the external

databases (f.e, OIM, GOCDB, BDII) and ADC configuration

information which can be modified.

The synchronization of AGIS content with the external

sources will be done by agents (data providers), agents

will access databases via standard interfaces.

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NEC2009 Varna- 10 September 2009AGIS Components

A.Anisenkov, D.Krivashin. Sep09

ATP

Logging Service



AGIS Information ATLAS clouds, tiers and sites

Topology : clouds, tiers, sites specifics (f.e. geography, names, etc)

Site Resources and Services information list of resources and services (FTS servers, SRM, LFC)

site service properties (name, status, type, endpoints)

Site information and configurations available CE and SE information (CPU,disk information, status, available resources)

availability and various status information, like site status in ATLAS Data distribution, Monte-Carlo Production,

Functional Tests. Site downtime periods

relations to currently running/planned tests, tasks or runs

Data replication. Sites shares and pairing. List of activities (f.e. reprocessing), activity start and end time • Global configuration

parameters needed by ADC applications

Users related information (privileges, roles, account info)

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NEC2009 Varna- 10 September 2009ATLAS Distributed Computing Monitoring

(Today)

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R.Rocha Sep09


NEC2009 Varna- 10 September 2009ATLAS Distributed Computing Monitoring

(Next)

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R.Rocha Sep09

• Simplify into one monitoring application (where it is possible)

• Standardize monitoring messageso https://svnweb.cern.ch/trac/da

shboard/wiki/WorkInProgresso HTTP for transporto JSON for data serialization

• Attempt to have a common (single) dashboard client applicationo Built using the Google Web

Toolkit (GWT)• Source data exposed directly from

its source (like the Panda database)o Avoid aggregation databases

like we have todayo Server side technology left

open



Summary & Conclusions

The ATLAS Collaboration has developed a set of software and middleware tools that enable access to data for physics analysis purposes to all members of the collaboration, independently of their geographical location.

Main building blocks of this infrastructure are

the Distributed Data Management system;

the Ganga and pathena for distributed analysis on the Grid.

Production System to (re)process and to simulate ATLAS data

Almost all required functionalities are already provided; and extensively used for simulated, as well as real data from beam and cosmic ray events.

Grid Information System technical proposal is finalized and the

system must be in production by the end of the year

Monitoring system standardization is in progress 22



МНОГО

БЛАГОДАРЮ

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Acknoledgements

Thanks to A.Anisenkov, D.Barberis, K.Bos, M.Branco, S.Campana, A.Farbin, J.Elmsheuser, D.Krivashin, A.Read, R.Rocha, A.Vaniachine, T.Wenaus,…

For pictures and slides used in this presentation

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Alexei Klimentov Brookhaven National Laboratory

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