Prototyping Virtual Data Technologies in ATLAS Data Challenge 1 Production K. De (UT Arlington), D. Malon (ANL), P. Nevski (BNL), A. Vaniachine (ANL)

Prototyping Virtual Data Technologies in ATLAS Data

Challenge 1 Production

K. De (UT Arlington), D. Malon (ANL), P. Nevski (BNL), A . Vaniachine (ANL)

CHEP 2003, March 24-28, La Jolla

Pavel Nevski (BNL/CERN) 2

ATLAS Data ChallengeComputational challenges facing the LHC experiments

are unprecedented. For ATLAS alone the raw data itself constitute 1.3 PB/year

To reduce the data management overhead a traditional centralized computing infrastructure would be simpler. In reality, CERN alone can handle only a fraction of these resources

The emerging World Wide computing model is embracing a global data and computation infrastructure to answer to the LHC computing needs



Distributed ProductionA significant fraction

of ATLAS Data Challenge 1 was

performed in a Grid environment

Grid technologies will naturally offer all the Collaboration members a uniform way of carrying out computing tasks

March 7, 2003 G. Poulard - ATLAS Sofware Week

ATLAS DC1/Phase I I : November 2002- J anuary 2003

Goals : Produce the data needed for the HLT TDRGet as many ATLAS institutes involved as possible

Worldwide collaborative activity

Participation : 56 Institutes

Australia Austria Canada CERN China Czech Republic Denmark * France Germany Greece I srael

I taly J apan Norway * Poland Russia Spain Sweden * Taiwan UK USA *

* using Grid



Centralized Management

For efficiency of the large production tasks distributed worldwide, it is essential to establish a centralized production management tools

The ATLAS Metadata Catalogue AMI and the Replica Catalogue Magda exemplify such Grid tools deployed in DC1

To complete the data management architecture for the distributed production ATLAS prototyped Virtual Data services



Introducing Virtual DataPrevailing views have been data-centric: we need to produce

the data (ASAP), recipes are just some tools that were used in the process. Their value has not been fully appreciated.

Preparation of recipes for data production requires significant efforts and encapsulates a considerable knowledge. Because the production recipes have to be fully validated, in DC0 it took more time to develop proper recipes than to run the production

The GriPhyN project (www.griphyn.org) introduced a different perspective:

recipes are as valuable as the dataIf you have the recipes (Virtual Data) you do not need the

data: you can reproduce the data ‘on-demand’



Data Management Architecture

AMI

ATLAS Metadata Interface

MAGDA

MAnager MAnager for Grid-for Grid-

based DAtabased DAta

Virtual Data Catalog

Prototype:

COOKBOOK

Collection of production recipes - COOKBOOK -complements ATLAS Grid tools



Certified Production RecipesFor each data transformation step in the DC1

processing pipeline the essential content of the verified data production recipes was captured and preserved in a COOKBOOK database

During the DC1 production, the COOKBOOK database server delivered in a controlled way the validated production parameters and the templated production recipes for thousands of the event generation and the detector simulation jobs around the world, simplifying production management



AthenaGenerators

HepMC.root

digis.zebra

atlsimatlsim pileup

digis.root Athenarecon

recon.root

QA.ntuple

geometry.zebraAthena QA

AthenaAtlfast

filtering.ntuple

geometry.root

Athenaconversion

QA.ntuple

Athena QA

Atlfast.root

Atlfastrecon

recon.root

Fully implemented DC1 workflow comprised of multiple

independent data transformation steps is complicated

Data-driven Workflow



Benefits of TechnologyDue to the innovative nature of Virtual Data services project

the data volume allocated for the production test of the system was limited to about one fifth of all the DC data

The major benefit of Virtual Data technologies was demonstrated by simplifying the management of the parameter collections that were different for each of the more than two hundred datasets produced in DC1

Significant reduction in the parameter management overhead enabled successful processing of about half of all the DC1 datasets using the Virtual Data services prototype



Data Reproducibility

Another benefit of Virtual Data technologies is the simplification of the data reprocessing step

We have found it useful to distinguish (both conceptually and in design) the data required before the invocation of the transformation from the data provenance information collected during and after the data transformation



Knowledge Management

For each major data transformation steps identified in the ATLAS data processing pipeline (event generation, detector simulation, background pile-up and digitization, etc) the COOKBOOK catalogue encapsulates the specific data transformation knowledge and the validated parameters settings that must exist before the transformation can be invoked



Virtual Data Pipeline

1. Event generation step: templated jobOptions

2. Simulation step: transformation attributes



Prototype Production System High-throughput features:

• scatter-gather data processing architecture Fault tolerance features:

• independent agents• pull-model for agent tasks assignment (vs push)• local caching of output and input data (except Objy)

ATLAS DC0 and DC1 parameter settings for simulations are recorded in the Virtual Data Catalog prototype database -COOKBOOK - using normalized components: parameter collections that are structured “orthogonally”

• Data reproducibility• Application complexity• Grid location



Production Experience Jobs accessing COOKBOOK VDC prototype ~8000 Hosts accessing COOKBOOK VDC prototype ~700 Domains accessing COOKBOOK VDC prototype 32:R1951.sc02.org, bu.edu, cacr.caltech.edu, cern.ch, cnaf.infn.it,cs.wisc.edu, dhcp.fnal.gov, dyn.optonline.net, fnal.gov,gridpp.rl.ac.uk, hep.anl.gov, hep.man.ac.uk, ihep.su, in2p3.fr,iu.edu, lip.pt, nersc.gov, nhn.ou.edu, phys.ufl.edu, phys.unm.edu,phys.uwm.edu, physics.indiana.edu, physics.lsa.umich.edu,quark.lu.se, roma1.infn.it, sinp.msu.ru, uchicago.edu,ucs.indiana.edu, ucsd.edu, uits.iupui.edu, usatlas.bnl.gov, uta.edu

Countries: CH, IT, FR, PT, RU, SE, UK, US

Many thanks to ATLAS collaborators who tried, testedand used the COOKBOOK VDC prototype!



Fault Tolerance Improvement

Given that the production system relied on the COOKBOOK VDC prototype running at one central location (at CERN), the reported failure rate due to such ‘single point of failure’ architecture was remarkably low (better than 0.001) over the whole DC1 production period

Further improvement in the VDC access robustness may be achieved by deploying catalog replicas at different geographic locations



Integrated Solutions

Database deployment proposal for ATLAS Data Challenges

Slide by Luc Goossens



Roadmap to SuccessBased on the positive experience with Virtual Data

technologies prototyping in DC1 where a significant contribution to the production both from US ATLAS and CERN have been done using the VDC, the COOKBOOK database is considered for deployment in ATLAS Data Challenges

We envision that the production recipe knowledge encapsulated in the COOKBOOK database will be integrated in a uniform system utilizing the Chimera technology from GriPhyN project eliminating 'manual' tracking of the data dependencies between separate production steps and enabling multi-step compound data transformations 'on-demand'

Documents

Prototyping Virtual Data Technologies in ATLAS Data Challenge 1 Production K. De (UT Arlington), D. Malon (ANL), P. Nevski (BNL), A. Vaniachine (ANL)