ATLAS-specific functionality in Ganga- Requirements for distributed analysis- ATLAS considerations- DIAL submission from Ganga- Graphical interfaces- Next steps

K. HarrisonCERN, 26th May 2005

26th May 2005 2

Requirements for distributed analysis

-General requirements for distributed analysis similar for ATLAS and LHCb

Deploy and run software based on Gaudi/Athena framework, and developed in terms of CMT packagesLocate data for analysisRun on a variety of backendsManage job output

-Differences in the choices of how to do the aboveNeed for experiment-specific functionality in

Ganga

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ATLAS considerations (1)

-Installation of ATLAS software is performed using pacman

Binary installation is 8-10Gb, so installation not to be taken lightlyPacman tool also being tested by LHCb

-Data-management system (Don Quijote) for ATLAS still under development

Must manage data distributed over different Grids• Today: LCG, NorduGrid, Grid 3 (US)

Have metadata catalogue (AMI) similar to Bookkeeping Database of LHCb, and various other catalogues

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ATLAS considerations (2)

-Two systems for distributed data processing developed

Production system used for collaboration-wide managed activities

•Similarities with DIRAC in LHCbATLAS Distributed Analysis (ADA) system, based

on DIALOverlaps between the two systems, and optimal

way of using them still being understood-In Ganga, two systems can be treated as different

backends

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- The production database, which contains abstract job definitions;

- The windmill supervisor that reads the production database for job definitions and present them to the different GRID executors in an easy-to-parse XML format;

- The Executors, one for each GRID flavor, that receive the job-definitions in XML format and convert them to the job description language of that particular GRID;

- Don Quijote, the Atlas Data Management System, moves files from their temporary output locations to their final destination on some Storage Element and registers the files in the Replica Location Service of that GRID.

-In order to handle the task of ATLAS DC2 an automated production system was designed

-The ATLAS production system consists of 4 components:

ATLAS production system

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ADA model

ROOT PYTHON

Catalogs DIAL AS ATPROD AS ARDA AS

LSF, CONDOR gLiteWMSATPROD

GUI andcommand lineclients

High level servicesfor cataloging andjob submission andmonitoring

Workloadmanagementsystems

AJDL

sh SQL gLite

AJDL

AJDL

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Ganga in ATLAS

Ganga User interface for job-related operations: configuration, submission, splitting, merging, monitoring, output retrieval, etc

LSFGrid 3 NorduGrid PBS BQSOtherLCG DIAL

Prod

uctio

n Sy

stem Condor

Other cataloguesand repositories

DIAL cataloguesand repositories

ATLAS Metadata Interface(AMI)

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Characteristics of DIAL

-For Ganga 4, have first concentrated on job submission to DIAL backend

-DIAL makes computing facilities available via web services-Server and client functionality implemented in C++

PyDial packaged developed to provide Python bindings to the C++ classes, and functions to simplify their use

-A job in DIAL is defined in terms of:Application: specifies software to be runTask: specifies configuration information (e.g. job options)Dataset: specifies data to be processedAbove map to DIAL objects, each with XML representation

-Information for applications, tasks and datasets is catalogued

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DIAL submissionfrom Ganga (1)

-To enable DIAL submission from Ganga, basically needed to implement the following:

Plug-in that uses ADA/DIAL job descriptionPlug-in that interacts with DIAL backendMapping between the two plug-ins

-To be able to store DIAL objects in Ganga job repository, needed object-to-XML converters-In Ganga, create instances of DIAL catalogues

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DIAL submissionfrom Ganga (2)

-From Ganga can do following:Query the DIAL cataloguesSubmit jobs to (remote) DIAL servicesRetrieve job output (also partial results)Via pyROOT, display output histogramsKeep track of jobs from one session to the next

Works well

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DIAL submissionfrom Ganga (3)

-Current syntax slightly different, but the idea is to be able to do as follows

>>> j = AdaJob()>>> j.application = “atlasopt”>>> j.task = “atlas_release jo.py”>>> j.dataset = “hma.dc2.003007.digit.A1.z_ee.aod-904.10files”>>> j.backend.schedulerURL = “lxgate21.cern.ch.20014”>>> j.submit()>>> print j.status>>> print j.result>>> j.copyResult( “myDirectory” )

- Have shown commands given at Python promptSame commands can be used in a script

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Ganga 4:graphical interfaces

-Priority for Ganga 4 has been to have infrastructure in place, and to have functionality available at the command line

Ganga 4 public release planned for June, without GUI

-Graphical tools for job configuration and monitoring have been developed for ADA (Alvin Tan)

For jobs using ADA/DIAL job description, expect this to be reused directly in GangaProvides a starting point for developing further

graphical tools for Ganga

Graphical job builder

Job monitoring

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Next steps (1)

-Priorities need to be discussed, but there are a number of possibilities

Enable Ganga submission to production system•Python tools for entering requests in production

database already written (Frederic Brochu)Enable direct submission from Ganga to one or

more Grid flavoursConnect ADA graphical tools to GangaUnderstand interaction with data-management

systemGet release out, so that people can try it

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Next steps (2)

-ATLAS review of distributed analysis tentatively scheduled for July

Must have release for this-Have summer student at Cavendish Laboratory for

6-8 weeks from end of June (Ruth Dixon del Tufo)Will test usability of Ganga, and help with

improvements