1. The GANGA Interface for ATLAS/LHCb Project overview Design details Component descriptions Interfaces Refactorisation plans ARDA Roger W L Jones ( Lancaster University) For the GANGA Team

2. 22 September 2003GridPP8 meeting, Bristol 2 The Project Ganga-related information regularly updated on web site http://ganga.web.cern.ch/ganga Ganga-related information regularly updated on web site http://ganga.web.cern.ch/ganga Ganga is being developed as a joint project between the ATLAS and LHCb experiments Began in the UK supported by GridPP, important collaborations with US colleagues Ganga is being developed as a joint project between the ATLAS and LHCb experiments Began in the UK supported by GridPP, important collaborations with US colleagues Current main contributors are: Developers: K.Harrison, A.Soroko, C.L.Tan (GridPP funded) Technical input and consulation: W.T.L.P.Lavrijsen, J.Martyniak, P.Mato, C.E.Tull GridPP coordination: N.Brook, R.W.L.Jones, G.N.Patrick Current main contributors are: Developers: K.Harrison, A.Soroko, C.L.Tan (GridPP funded) Technical input and consulation: W.T.L.P.Lavrijsen, J.Martyniak, P.Mato, C.E.Tull GridPP coordination: N.Brook, R.W.L.Jones, G.N.Patrick

3. 22 September 2003GridPP8 meeting, Bristol 3 Motivation and Background ATLAS and LHCb develop applications within a (complex but powerful) common framework: Gaudi/Athena Both collaborations aim to exploit potential the of the Grid for large-scale, data-intensive distributed computing ATLAS and LHCb develop applications within a (complex but powerful) common framework: Gaudi/Athena Both collaborations aim to exploit potential the of the Grid for large-scale, data-intensive distributed computing Simplify management of analysis and production jobs for end-user physicists by developing tools for accessing Grid services with built-in knowledge of how Gaudi/Athena works: Gaudi/Athena and Grid Alliance (GANGA) Also aid job creation, submission, management and archival in non- Grid contexts Generic components, especially those for interfacing to the Grid, can be used in other experiments Simplify management of analysis and production jobs for end-user physicists by developing tools for accessing Grid services with built-in knowledge of how Gaudi/Athena works: Gaudi/Athena and Grid Alliance (GANGA) Also aid job creation, submission, management and archival in non- Grid contexts Generic components, especially those for interfacing to the Grid, can be used in other experiments

4. 22 September 2003GridPP8 meeting, Bristol 4 Milestones GAUDI/Athena application GANGA GUI JobOptions Algorithms Collective & Resource Grid Services Histograms Monitoring Results Summer 2001: First ideas for Ganga Spring 2002: Work on Ganga started, with strong support from GridPP Spring 2003: GANGA1 released for user evaluation Autumn 2003: Refactorisation for GANGA2 – 3 week workshop, BNL Summer 2004: Use of GANGA2 in Data Challenges and Computing Model tests Summer 2001: First ideas for Ganga Spring 2002: Work on Ganga started, with strong support from GridPP Spring 2003: GANGA1 released for user evaluation Autumn 2003: Refactorisation for GANGA2 – 3 week workshop, BNL Summer 2004: Use of GANGA2 in Data Challenges and Computing Model tests

5. 22 September 2003GridPP8 meeting, Bristol 5 Ganga Deployment Ganga has been installed and used at a number of sites: Birmingham, BNL, Cambridge, CERN, Imperial, LBNL, Oxford Ganga interfaces to several Grid implementations: EDG; Trillium/US-ATLAS; NorduGrid under test Ganga interfaces to local batch systems: LSF, PBS Ganga has been used to run a variety of LHCb and ATLAS applications: LHCb analysis (DaVinci) ATLAS reconstruction ATLAS full and fast simulation Ganga has been used to run BaBar applications (see talk by J.Martyniak) A Ganga tutorial was given at BNL (US-Atlas Computing and Physics meeting), with some 50 participants, all of whom successfully used Ganga to submit jobs (at the same time) Ganga has been installed and used at a number of sites: Birmingham, BNL, Cambridge, CERN, Imperial, LBNL, Oxford Ganga interfaces to several Grid implementations: EDG; Trillium/US-ATLAS; NorduGrid under test Ganga interfaces to local batch systems: LSF, PBS Ganga has been used to run a variety of LHCb and ATLAS applications: LHCb analysis (DaVinci) ATLAS reconstruction ATLAS full and fast simulation Ganga has been used to run BaBar applications (see talk by J.Martyniak) A Ganga tutorial was given at BNL (US-Atlas Computing and Physics meeting), with some 50 participants, all of whom successfully used Ganga to submit jobs (at the same time)

6. 22 September 2003GridPP8 meeting, Bristol 6 Related Activities AthAsk: Creating a Gaudi/Athena job is in itself complicated, AthAsk wraps the complexity, incorporating knowledge of the component applications DIAL: DIAL focuses on the requirements for interactive Grid analysis DIAL complements Ganga (focus on non-interactive processing), and work has started on a Ganga-DIAL interface to combine the best of both Chimera: Providing a Chimera-driven production and analysis workflow system for ATLAS Automated installation and packaging with CMT&pacman Needed for many-site code maintenance and to distribute user code and run- time environment Looking at ways to make use of pacman from Ganga AtCom: Interim production tool 02/03 GridPP development effort GANAG test bed AthAsk: Creating a Gaudi/Athena job is in itself complicated, AthAsk wraps the complexity, incorporating knowledge of the component applications DIAL: DIAL focuses on the requirements for interactive Grid analysis DIAL complements Ganga (focus on non-interactive processing), and work has started on a Ganga-DIAL interface to combine the best of both Chimera: Providing a Chimera-driven production and analysis workflow system for ATLAS Automated installation and packaging with CMT&pacman Needed for many-site code maintenance and to distribute user code and run- time environment Looking at ways to make use of pacman from Ganga AtCom: Interim production tool 02/03 GridPP development effort GANAG test bed

7. 22 September 2003GridPP8 meeting, Bristol 7 General Design Characteristics The user interacts with a single application covering all stages of a job’s life-time The design is modular Although Atlas and LHCb use the same framework and the same software management tool, there are significant differences in what is expected from the software, and Ganga must have the flexibility to cope Ganga provides a set of tools to manipulate jobs and data. Tools are accessible from CLI (other scripts) or from GUI Ganga allows access both to the local resources (e.g., LSF batch system) and to the GRID Should follow, and contribute to, developments in LHC Computing Grid  Implementation is in Python The user interacts with a single application covering all stages of a job’s life-time The design is modular Although Atlas and LHCb use the same framework and the same software management tool, there are significant differences in what is expected from the software, and Ganga must have the flexibility to cope Ganga provides a set of tools to manipulate jobs and data. Tools are accessible from CLI (other scripts) or from GUI Ganga allows access both to the local resources (e.g., LSF batch system) and to the GRID Should follow, and contribute to, developments in LHC Computing Grid  Implementation is in Python

8. 22 September 2003GridPP8 meeting, Bristol 8 Value Added Single point of entry for configuring and running different types of ATLAS and LHCb jobs, uniform approach It helps with job definition and configuration Common task and user-defined templates Application-specific job options, or user-supplied job-options file Editing of job-option values, guided to meaningful values for some applications Simple, flexible procedure for splitting and cloning jobs Accepts user-provided script for splitting/cloning Bookkeeping and stored job settings Persistent job representation for archive & exchange Automatic monitoring, job status query on local and distributed systems Extensible framework for job-related operations Integration of new services, and for the assimilation of contributions from users Single point of entry for configuring and running different types of ATLAS and LHCb jobs, uniform approach It helps with job definition and configuration Common task and user-defined templates Application-specific job options, or user-supplied job-options file Editing of job-option values, guided to meaningful values for some applications Simple, flexible procedure for splitting and cloning jobs Accepts user-provided script for splitting/cloning Bookkeeping and stored job settings Persistent job representation for archive & exchange Automatic monitoring, job status query on local and distributed systems Extensible framework for job-related operations Integration of new services, and for the assimilation of contributions from users

9. 22 September 2003GridPP8 meeting, Bristol 9 Software Bus Design User has access to functionality of Ganga components through GUI and CLI, layered one over the other above a Software Bus Software Bus itself is implemented as a Python module Components used by Ganga fall into 3 categories: Ganga components of general applicability or core components (to right in diagram) Ganga components providing specialised functionality (to left in diagram) External components (at bottom in diagram) User has access to functionality of Ganga components through GUI and CLI, layered one over the other above a Software Bus Software Bus itself is implemented as a Python module Components used by Ganga fall into 3 categories: Ganga components of general applicability or core components (to right in diagram) Ganga components providing specialised functionality (to left in diagram) External components (at bottom in diagram) Job Definition Job Registry Job Handling File Transfer Python Native Software Bus CLI GUI Py ROOT Gaudi Python PyCMT PyAMI Py Magda BaBar Job Definition and Splitting Gaudi/Athena Job Options Editor Gaudi/Athena Job Definition

10. 22 September 2003GridPP8 meeting, Bristol 10 Generic Components (1) Components may have uses outside ATLAS and LHCb Core component provides classes for job definition, where a job is characterised in terms of: name, workflow, required resources, status Workflow is represented as a sequence of elements (executables, parameters, input/output files, etc) for which associated actions are implicitly defined Required resources are specified using a generic syntax Future workflow will merge with DIAL and Chimera Components may have uses outside ATLAS and LHCb Core component provides classes for job definition, where a job is characterised in terms of: name, workflow, required resources, status Workflow is represented as a sequence of elements (executables, parameters, input/output files, etc) for which associated actions are implicitly defined Required resources are specified using a generic syntax Future workflow will merge with DIAL and Chimera

11. 22 September 2003GridPP8 meeting, Bristol 11 Generic Components (2) Job-registry component allows for storage and recovery of job information, and allows for job objects to be serialized Multi-threaded environment based on Python threading module Serialisation of objects (user jobs) is implemented with the Python pickle module Script-generation component translates a job's work flow into the set of instructions to be executed when the job is run Job-submission component submits work flow script to target batch system, creates JDL file and translates resource requests EDG, Trillium/US-ATLAS, LSF, PBS Can submit, monitor, and get output from GRID jobs File-transfer component handles transfer between sites of input & output files, adds commands to work flow script on submission Job-monitoring component performs queries of job status Should move to R-GMA Local/batch job monitoring problematic; move to job pushing info to specified location, integrate with NetLogger for Grid Job-registry component allows for storage and recovery of job information, and allows for job objects to be serialized Multi-threaded environment based on Python threading module Serialisation of objects (user jobs) is implemented with the Python pickle module Script-generation component translates a job's work flow into the set of instructions to be executed when the job is run Job-submission component submits work flow script to target batch system, creates JDL file and translates resource requests EDG, Trillium/US-ATLAS, LSF, PBS Can submit, monitor, and get output from GRID jobs File-transfer component handles transfer between sites of input & output files, adds commands to work flow script on submission Job-monitoring component performs queries of job status Should move to R-GMA Local/batch job monitoring problematic; move to job pushing info to specified location, integrate with NetLogger for Grid

12. 22 September 2003GridPP8 meeting, Bristol 12 Experiment-Specific Components GaudiApplicationHandler Can access Configuration DB for some Gaudi applications, using the xmlrpclib module Ganga can create user-customized Job Options files using this DB Intelligent Job Options editor exists for some applications Specialised application handlers exist for ATLAS fast simulation and for LHCb analysis Components incorporate knowledge of the experiments’ Gaudi/Athena framework Gaudi job definition component adds to workflow elements in general- purpose job-definition component, e.g. dealing with configuration management; also provides workflow templates covering common tasks Other components provide for job splitting, and output collection Job splitting may have generic aspects; will be investigated in collaboration with DIAL More work needed on job merging GaudiApplicationHandler Can access Configuration DB for some Gaudi applications, using the xmlrpclib module Ganga can create user-customized Job Options files using this DB Intelligent Job Options editor exists for some applications Specialised application handlers exist for ATLAS fast simulation and for LHCb analysis Components incorporate knowledge of the experiments’ Gaudi/Athena framework Gaudi job definition component adds to workflow elements in general- purpose job-definition component, e.g. dealing with configuration management; also provides workflow templates covering common tasks Other components provide for job splitting, and output collection Job splitting may have generic aspects; will be investigated in collaboration with DIAL More work needed on job merging

13. 22 September 2003GridPP8 meeting, Bristol 13 Job handling: splitting a job (lots of potential reuse) Template Job Repository Splitting script 1 Splitting script 2 Splitting script … Job Handling module Subjob 1 Subjob 2 Subjob 3 Subjob 4 Subjob … Selects or creates

14. 22 September 2003GridPP8 meeting, Bristol 14 External Components Additional functionality is obtained using components developed outside of Ganga: Modules of python standard library Non-python components for which appropriate interface has been written Gaudi framework itself (GaudiPython) Analysis package, ROOT (PyROOT) Configuration management tool (CMT) ATLAS metadata interface, AMI (PyAMI) ATLAS manager for Grid data, Magda (PyMagda) Additional functionality is obtained using components developed outside of Ganga: Modules of python standard library Non-python components for which appropriate interface has been written Gaudi framework itself (GaudiPython) Analysis package, ROOT (PyROOT) Configuration management tool (CMT) ATLAS metadata interface, AMI (PyAMI) ATLAS manager for Grid data, Magda (PyMagda)

15. 22 September 2003GridPP8 meeting, Bristol 15 Implementation of Components (1) JobHandler Requirements JobAttributes Credentials 1 1… Job JobsRegistryJobsCatalog Application GaudiApplicationHandler ParameterExecutable 1 0… 1 1 1 1 1… Job definition component Job registry componentJob handling component Specialised component: Gaudi/Athena job definition

16. 22 September 2003GridPP8 meeting, Bristol 16 Implementation of Components (2) JobHandler AnotherJobHandler GridJobHandler PBSJobHandlerLSFJobHandler LocalJobHandler Job handling component ApplicationHandler BaBarApplicationHandler GaudiApplicationHandler DaVinchiApplicationHandler AtlfastApplicationHandler Application specific components

17. 22 September 2003GridPP8 meeting, Bristol 17 Interfacing to the Grid Job class JobsRegistry class Job Handler class Data management service Job submission Job monitoring Security service dg-job-list-match dg-job-submit dg-job-cancel dg-job-list-match dg-job-submit dg-job-cancel grid-proxy-init MyProxy grid-proxy-init MyProxy dg-job-status dg-job-get-logging-info R-GMA dg-job-status dg-job-get-logging-info R-GMA edg-replica-manager dg-job-get-output globus-url-copy edg-replica-manager dg-job-get-output globus-url-copy EDG UI

18. 22 September 2003GridPP8 meeting, Bristol 18 Interfaces: CLI atlasSetup = GangaCommand(“source /afs/cern.ch/user/h/harrison/public/atlasSetup.sh”) atlfast = GangaCMTApplication(“TestRelease”, “TestRelease-00-00-15”,”athena.exe”, “run/AtlasfastOptions.txt”) atlfastOutput = GangaOutputFile(“atlfast.ntup”) workStep1 = GangaWorkStep([atlasSetup,atlfast,atlfastOutput]) workFlow = GangaWorkFlow([workStep1]) lsfJob = GangaLSFJob(“atlfastTest”,workFlow) lsfJob.build() lsfJob.run() atlasSetup = GangaCommand(“source /afs/cern.ch/user/h/harrison/public/atlasSetup.sh”) atlfast = GangaCMTApplication(“TestRelease”, “TestRelease-00-00-15”,”athena.exe”, “run/AtlasfastOptions.txt”) atlfastOutput = GangaOutputFile(“atlfast.ntup”) workStep1 = GangaWorkStep([atlasSetup,atlfast,atlfastOutput]) workFlow = GangaWorkFlow([workStep1]) lsfJob = GangaLSFJob(“atlfastTest”,workFlow) lsfJob.build() lsfJob.run() At the moment CLI is based on low- level tools; a higher-level set of commands is under development

19. 22 September 2003GridPP8 meeting, Bristol 19 Interfaces: GUI GUI has been implemented using wxPython extension module Layered on CLI All job configuration data are represented in a hierarchical structure accessible via tree control; most important job parameters are brought to the top of the tree “User view” provides easy access to the top-level parameters All job parameters defined by the user can be edited via GUI dialogs A help system has been implemented, using html browser classes from wxPython All implemented tools are available through the GUI, but some require a more elaborate interface, e.g., Job Options browser/editor Python shell is embedded into the GUI and allows user to configure interface from the command line GUI has been implemented using wxPython extension module Layered on CLI All job configuration data are represented in a hierarchical structure accessible via tree control; most important job parameters are brought to the top of the tree “User view” provides easy access to the top-level parameters All job parameters defined by the user can be edited via GUI dialogs A help system has been implemented, using html browser classes from wxPython All implemented tools are available through the GUI, but some require a more elaborate interface, e.g., Job Options browser/editor Python shell is embedded into the GUI and allows user to configure interface from the command line

20. 22 September 2003GridPP8 meeting, Bristol 20 Basic GUI Job tree Python interpreter Toolbar Main panel

21. 22 September 2003GridPP8 meeting, Bristol 21 Job creation

22. 22 September 2003GridPP8 meeting, Bristol 22 Job-parameters panel

23. 22 September 2003GridPP8 meeting, Bristol 23 Job-options editor: sequences

24. 22 September 2003GridPP8 meeting, Bristol 24 Job-options editor: options

25. 22 September 2003GridPP8 meeting, Bristol 25 Job submission Job position depends on monitoring info

26. 22 September 2003GridPP8 meeting, Bristol 26 Examination of job output

27. 22 September 2003GridPP8 meeting, Bristol 27 Job splitting User or “third party” splitting script is required GUI displays script descriptions, where these exist, to guide user choices If “split” function of the script accepts a parameter it is interpreted as a number of subjobs and can be entered in the job splitting dialogue User or “third party” splitting script is required GUI displays script descriptions, where these exist, to guide user choices If “split” function of the script accepts a parameter it is interpreted as a number of subjobs and can be entered in the job splitting dialogue

28. 22 September 2003GridPP8 meeting, Bristol 28 Ganga Help

29. 22 September 2003GridPP8 meeting, Bristol 29 Ganga Refactorisation Scheme can be broken down as follows: Definition of job options The user retrieves a set of job options from a database (or other standard location), and is then able to make modifications using an intelligent job-options editor; the result is a job-options template Definition of dataset The user selects the input dataset, obtaining information on the available datasets from a catalogue Definition of execution strategy The strategy might be selected from a database, or the user might provide a new strategy definition Creation of job-collection description An XML description of the job, or collection of jobs, to be submitted is created on the basis of the previously defined job- options template, input dataset and execution strategy Definition of job requirements Some standard requirements may be defined for each experiment The user may also specify requirements, for example imposing that jobs be submitted to a particular cluster Additional requirements may be derived from the job-collection description Job submission A dispatcher determines where to submit jobs, on the basis of the job-collection description and the job requirements, and invokes a service that returns the appropriate submission procedure Installation of software and components Experiment-specific software required to run the user job is installed as necessary on the remote client Any (Ganga) components required to interpret the job-collection description are also installed Job execution Agents supervise the execution and validation of jobs on the batch nodes Scheme can be broken down as follows: Definition of job options The user retrieves a set of job options from a database (or other standard location), and is then able to make modifications using an intelligent job-options editor; the result is a job-options template Definition of dataset The user selects the input dataset, obtaining information on the available datasets from a catalogue Definition of execution strategy The strategy might be selected from a database, or the user might provide a new strategy definition Creation of job-collection description An XML description of the job, or collection of jobs, to be submitted is created on the basis of the previously defined job- options template, input dataset and execution strategy Definition of job requirements Some standard requirements may be defined for each experiment The user may also specify requirements, for example imposing that jobs be submitted to a particular cluster Additional requirements may be derived from the job-collection description Job submission A dispatcher determines where to submit jobs, on the basis of the job-collection description and the job requirements, and invokes a service that returns the appropriate submission procedure Installation of software and components Experiment-specific software required to run the user job is installed as necessary on the remote client Any (Ganga) components required to interpret the job-collection description are also installed Job execution Agents supervise the execution and validation of jobs on the batch nodes

30. 22 September 2003GridPP8 meeting, Bristol 30 Future plans Database of Standard Job Options Job-Options Editor Job-Options Template Job-Options Knowledge Base Dataset Dataset Catalogue Dataset Selection Job Factory (Machinery for Generating XML Descriptions of Multiple Jobs) Strategy Selection Job Collection (XML Description) User Requirements Database of Job Requirements Derived Requirements Job Requirements Strategy Database (Splitter Algorithms) DispatcherScheduler Proxy Scheduler Service Remote-Client Scheduler Grid/ Batch-System Scheduler Agent (Runs/Validates Job) Software Cache Component Cache Software/Component Server Remote Client Local Client Execution node NorduGrid Local DIAL DIRAC Other JDL, Classads, LSF Resources, etc LSF PBS EDG USG Refactorisation of Ganga, with submission on remote client Motivation Ease integration of external components Facilitate multi-person, distributed development Increase Customizabili ty/Flexibility Allow GANGA components to be used externally more easily Motivation Ease integration of external components Facilitate multi-person, distributed development Increase Customizabili ty/Flexibility Allow GANGA components to be used externally more easily

31. 22 September 2003GridPP8 meeting, Bristol 31 Use of Components Outside of Ganga Ganga complies with recent requirements for grid services domain decomposition as described in the Architectural Roadmap towards Distributed Analysis (ARDA) document. Some Ganga components provide native services (API, UI) Majority of components just represent an uniform interface to the existent grid middleware services (e.g., Data Management, Job Monitoring) Ganga complies with recent requirements for grid services domain decomposition as described in the Architectural Roadmap towards Distributed Analysis (ARDA) document. Some Ganga components provide native services (API, UI) Majority of components just represent an uniform interface to the existent grid middleware services (e.g., Data Management, Job Monitoring) Service (ARDA)Provided by Ganga (native) Provided by Ganga (middleware interface) API and User InterfacePyBus, Ganga GUI, CLI Authentication, Authorisation and AuditingJobHandlers package Workload and Data Management SystemsJobHandlers package File and Metadata CataloguesFile and PyMagda modules Information serviceSoftware server Grid and Job Monitoring servicesJobHandlers package Storage and Computing elementsFile module Package Manager and Job provenance servicePyCMT, Packman modules

32. 22 September 2003GridPP8 meeting, Bristol 32 Future Plans GANGA prototype has had enthusiastic and demanding early adopters - A new release will provide an interim production version A refactorisation is now underway - Stricter adherence to the component model - Compliance with the draft LCG distributed analysis services model - Installation tools need to be interfaced, at least for user analysis code - Add new job handlers - Web-based variant GUI (or thin remote client) should be considered. Security issues need to be addressed in this case. - Exploit Grid Monitoring Architecture Components should be capable of wide reuse GANGA can deal with the ARDA framework Software installation for analysis jobs a priority Metadata query/selection/browsing and design a high priority GANGA prototype has had enthusiastic and demanding early adopters - A new release will provide an interim production version A refactorisation is now underway - Stricter adherence to the component model - Compliance with the draft LCG distributed analysis services model - Installation tools need to be interfaced, at least for user analysis code - Add new job handlers - Web-based variant GUI (or thin remote client) should be considered. Security issues need to be addressed in this case. - Exploit Grid Monitoring Architecture Components should be capable of wide reuse GANGA can deal with the ARDA framework Software installation for analysis jobs a priority Metadata query/selection/browsing and design a high priority