K. HarrisonCERN, 20th April 2004

AJDL interface and LCG submission

- Overview of AJDL- Using AJDL from Python- LCG submission

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AJDL basics- AJDL (Analysis Job Description Language) provides for a high-level description of job content

- Basic elements include: Application: executable to process data Task: user configuration of application Dataset: specifies input and output Configuration: resource requirements

- AJDL aims to define way in which job information is transmitted between software components: Allow multiple clients to communicate with independently implemented services by defining a common interface

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Client-service communication via AJDL

P I/S E A L

G A N G A

R O O T

J A S

C o m m a nd line

H ighle v e lJ D L

P R O O FG A N G A -LC G

C o nd o r-G

G C E /C him e raS T A RJ D A P (J A S )

D IA L-inte ra c tiv e

An a ly s is e n v iro n m e n ts S ch e d u le r s

G r idse rv ice s

A T LA S p ro d u c tio n

P lu g -inclie n ts

P o rta l/s w itc h

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Working with AJDL- AJDL represents job data using XML

- To do anything useful, need classes that interpret and manipulate the data C++ implementation of relevant classes provided by DIAL - User should need to know nothing (or very little) about AJDL, but will work with client tools that use AJDL in the background

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Analysis on a local batch system

- User workflow for running a Gaudi/Athena analysis on a local batch system might typically include the following steps: 1) Write code (Gaudi/Athena algorithm) 2) Specify job options 3) Select dataset 4) Provide job-execution script 5) Submit job to batch system, for example using: bsub < myJobScript 6) Intermittently query job status, for example using: bjobs 7) When job completes, look at output

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Analysis on a distributed system- Aim is that user workflow for running Gaudi/Athena analysis with distributed resources and datasets should be no more complicated than for analysis on local batch system

- In ADA, steps 1) to 4) of workflow become as follows; Steps 1) and 2) Define task Step 3) Define dataset Step 4) Define application

- Client tools should help with these, and should deal with job submission, status queries and output retrieval

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AJDL and Ganga

- Ganga already has its own job-description scheme, and and job-related classes, as do ATLAS and LHCb production systems Largely subjective as to which scheme is better

- AJDL not needed internally by Ganga, but is essential for interacting with ATLAS analysis services

- Two approaches possible: 1) Use current Ganga job description internally, and translate to/from AJDL as needed 2) Adopt AJDL internally also Second approach currently being investigated

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Using AJDL from Python (1)

- Two approaches to supporting AJDL from Python:

1) Provide Python bindings for C++ implementation of DIAL Advantages: - Makes use of work already done - Little maintenance required (on Python side) - Demonstrates possibility to use Python with non-Python components Disadvantage: - Lose portability: rely on significant number of shared libraries, and must recompile on each platform

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Using AJDL from Python (2)

2) Write Python implementation of AJDL Advantages: - Portable Disadvantages: - Work has to start from scratch - Code must be maintained, to reflect any future changes in AJDL

- Ideally, probably want both solutions: Python bindings for C++ implementation to give most complete functionality Minimal Python implementation to allow portability

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Binding DIAL C++ implementation of AJDL (1)

- Start by providing Python bindings for DIAL C++ implementation of AJDL

- From outside Python, use lcgdict command, provided by SEAL, to generate LCG dictionaries from C++ class header files

- Compile dictionary files (suffix _dict.cpp) to produce shared-object libraries

- Construct Python package, with initialisation file that uses PyLCGDict/PyLCGDict2 from SEAL to load libraries and allow class access

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Binding DIAL C++ implementation of AJDL (2)

- Dictionaries and libraries successfully created for DIAL AJDL classes, with help from M.Marino- Have been able to create AJDL objects from Python/Ganga, and have tested some simple methods- Some limitations with current binding software: Not possible to import free functionsProblem addressed in newer version of Reflection package? Overloaded << operator not mapped to __str__ Will find workaround to allow object printing Mapping between std::string and Python stringWorks in PyLCGDict, not yet available with PyLCGDict2- More extensive testing to start soon

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Job submission to LCG- Submission of LHCb analysis jobs under LCG1 tested successfully outside of Ganga

- Data for analysis copied in advance to preselected destination site

- Needed only very simple JDL file: Executable = "/usr/bin/python2.2"; Arguments = "DaVinci.py"; StdOutput = "std.out"; StdError = "std.err"; InputSandbox = {"DaVinci.py","InstallDaVinci.py","SoftwareDistribution.py","RunDaVinci.py","DaVinci.opts"}; OutputSandbox = {"std.out","std.err"}; Requirements = other.GlueCEUniqueID == "farm012.hep.phy.cam.ac.uk:2119/jobmanager-lcgpbs-cgSq";

Python modules loaded in sandbox take care of installing required softwae and running analysis executable

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LCG submission from Ganga

- In Ganga, could use job handler very similar to the one used for EDG submission (or an improved version of this)

- For ATLAS, would want to add an AJDL interface

- Need to understand if Ganga should continue to provide components for directly interacting with Grid/batch systems Might be better to use components developed for ATLAS and LHCb production systems