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e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
e-Science for the Science Kilometre Array
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e-Science for the Science Kilometre Array

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How e-Science tools are needed for the new data intensive science, specifically targeted to the Square Kilometre Array. Talk given at the Special Symposium 15 on Data Intensive Astronomy, held during …

How e-Science tools are needed for the new data intensive science, specifically targeted to the Square Kilometre Array. Talk given at the Special Symposium 15 on Data Intensive Astronomy, held during the General Assembly Meeting of the International Astronomical Union in Bejing, 2012.

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  • 1. IAU 2012 Data Intensive Astronomy Symposium (Sp15)Beijing, August 29th 2012e-Science for theSquare Kilometre ArrayJuan de Dios Santander Vela (IAA-CSIC)on behalf of the AMIGA team
  • 2. Talk OverviewThe Square Kilometre Array (SKA)The SKA ChallengeAMIGA & SKAe-Science Tools for the SKASKA Computing SynergiesConclusions
  • 3. The Square Kilometre Array
  • 4. The Square Kilometre ArrayThe embodiment of The Hydrogen Array conceptThousands of antennas, with up to1 sq km collecting area Distributed across thousands of kilometres of terrainWith enormous simultaneous bandwidth toincrease survey speed LE, RK CA WO L S ET NTA R NCan be incrementally built T INE NSO ON C ED SE A UT RIB DIST
  • 5. SKA Antennas COMBINATION OF DIFFERENT ANTENNA KINDS Low-Frequency Aperture Arrays Sparse aperture arrays 70 – 450 MHz Multibeaming SKA1: 2016 -2019Mid-frequency dishes 13m Gregorian-offset dishes 450 MHz – 3 GHz Surface accuracy to 10-25 GHz
  • 6. SKA Antennas Mid-Frequency Aperture Arrays Dense aperture arrays 200 – 500 MHz 200 deg2 FoV
  • 7. SKA Antennas Mid-Frequency Aperture Arrays Dense aperture arrays 200 – 500 MHz 200 deg2 FoV SKA2: 2018 -2023Focal Plane Arrays Multibeam Radio-Camera 12m antennas 700 MHz – 1.8 GHz Surface accuracy to 10 GHz
  • 8. SKA Site Selection SOUTH-AFRICA & AUSTRALIA/NEW ZEALAND JOINT SITE
  • 9. SKA Site Selection SKA2 AAS SKA2 MID SKA1&2 MID SKA1 LOWSKA1 SKA2 SKA1 SURVEYSKA1_LOW ANZ SKA2_LOW ANZSKA1_MID RSA SKA2_MID RSASKA1_SURVEY ANZ SKA2_AA RSA
  • 10. The SKA Challenge
  • 11. MASSIVE DATA FLOW, STORAGE & PROCESSING SKA2 wide area data flow 16 Tb/s 4 Pb/s 20 Gb/s 24 Tb/s 20 Gb/s COURTESY A. FAULKNER June 2012 AA Power Challenges Workshop
  • 12. SKA2 wide area data flow 16 Tb/s 4 Pb/s 20 Gb/s 24 Tb/s 20 Gb/s
  • 13. SKA ARCHITECTURE DIAGRAM(SKA1 HIGH-LEVEL OVERVIEW) Figure 3: An SKA1 system block diagram.
  • 14. POSSIBLE SCIENCE WILL DEPEND ON SOFTWARE CAPABILITIES SOFTWARE DRIVENFigure 3: An SKA1 system block diagram.
  • 15. INTERFACE TO THE USERSFigure 3: An SKA1 system block diagram.
  • 16. But, how can we doour science, then?
  • 17. AMIGA & SKA
  • 18. AMIGAAnalysis of the interstellar Medium of Isolated GAlaxies Multi-wavelength, multi-object study on isolated galaxies with strict isolation criteria Careful curation of data Very careful processing of new parameters from Group’s own observation programs and data reduction Literature table scanning Virtual Observatory table harvesting and parsing Emphasis on marrying astronomy and computer science, and buy-in of the VO SERS CE U E-SCIEN
  • 19. PI, L. VERDES-MONTENEGROAMIGA REVISE HER TALK FOR MAIN RESULTS (SPS3, SECULAR EVOLUTION)Analysis of the interstellar Medium of Isolated GAlaxies Multi-wavelength, multi-object study on isolated galaxies with strict isolation criteria Careful curation of data Very careful processing of new parameters from Group’s own observation programs and data reduction Literature table scanning Virtual Observatory table harvesting and parsing Emphasis on marrying astronomy and computer science, and buy-in of the VO ERS! ELOP NCE DEV E- SCIE
  • 20. AMIGAProject goal: providing a baseline for galaxyproperties to compare with other environmentsInteraction-free sample, ideal for tracing HI infall:we can use CIG galaxies to detect the cosmic webNeed for very sensitive telescopes able to resolvefaint HI ➡ Square Kilometre Array & pathfinders WE NEED TOOLS FOR OUR OWN SCIENCE ANALYSIS ⤷ PARTICIPATING IN SKA.TEL.SDP PROTOCONSORTIUM
  • 21. e-Science Tools & SKA
  • 22. e-Science Tools & SKADistributed computing Move computation to the data Computing services ➡ Science-computing }Collaborative environments FOR SCIENTIFIC DISCUSSION &Linked data SCIENCE EXTRACTION
  • 23. Defining ComputationsEvents & Processes FORMALLY, OR AT LEASTDependencies MACHINE READABLE Resources ➡ Local & Remote Processes Sequences WORKFLOW Concurrences DEFINITION Triggers LANGUAGES
  • 24. AMIGA ContributionsWf4Ever Workflows for process & scientific methodology specification Web and command line tools for data preservation, metho- dology preservation, reuse, repurposing, & collaboration Provide extra tools for astronomical data processing & servicesAMIGA4GAS Use workflows as process abstraction engines Use federation and supercomputing models for Taverna Adapt Taverna (& workflows) to those computing models
  • 25. EU FUNDED FP7 STREP PROJECT DECEMBER 2010 – DECEMBER 2013 1. Intelligent Software Components 2 (iSOCO, Spain) 7 5 4 2. University of Manchester (UNIMAN, UK) 3. Universidad Politécnica de Madrid13 (UPM, Spain) 6 4. Poznan Supercomputing and Networking Centre (PSNC, Poland) 5. University of Oxford (OXF, UK) 6. Instituto de Astrofísica de Andalucía (IAA, Spain) 7. Leiden University Medical Centre (LUMC, NL)
  • 26. Technological infrastructure for the preservation and efficient retrieval and reuse of scientific workflows in a range of disciplinesPartners Goals• One SME Archival, classification, and indexing• Six public organisations of scientific workflows and their associated materials in scalableCore Competencies (Tech) semantic repositories, providing• Digital Libraries advanced access and recommendation• Workflow Management capabilities• Semantic Web• Integrity & Authenticity Creation of scientific communities to• Provenance collaboratively share, reuse, and evolve• Information Quality workflows and their parts, stimulating the development of new scientific ISHEDCase Studies knowledge STABL E ADY E RIMENT, N G ALR YEXPE• Astronomy (IAA-CSIC) TA RGETI ITIES: M N ATORY• Genome-wide Analysis and Biobanking C OMMU AL OBSERV VIRTU
  • 27. Fork Me on GitHubAstroTavernaAstronomy plugins for Taverna WorkbenchAstroTavernaTo install the AstroTaverna plugin to Taverna: Download and install Taverna 2.4 Start Taverna Add a plugin site: http://wf4ever.github.com/astrotaverna/ Restart Taverna The VO services perspective should now appear together with variouys local tools under Available ServicesFor more information, see the Astrotaverna wiki page.AstroTaverna maintained by wf4everPublished with GitHub Pages
  • 28. Fork Me on GitHubAstroTavernaAstronomy plugins for Taverna WorkbenchAstroTavernaTo install the AstroTaverna plugin to Taverna: Download and install Taverna 2.4 Start Taverna Add a plugin site: http://wf4ever.github.com/astrotaverna/ Restart Taverna The VO services perspective should now appear together with variouys local tools under Available ServicesFor more information, see the Astrotaverna wiki page.AstroTaverna maintained by wf4everPublished with GitHub Pages
  • 29. AMIGA4GAS3D Kinematical modeling 12 ASCII Files ROTCUR GALMOD Input Files 12 Runs 12 Cubes Possible combinations • 4 Approaching in Input Parameters • 4 Receeding • 4 Both COPY 1 DataCube 1 Velocity Map 1 Config File Rotcur 1 Config File Galmod MOMENTS 8 Velocity Maps 8 Cubes • 4 Approaching + Receeding • 4 Both 00SUB 00SUBVARIABLE PARAMSINSETRADII, WIDTHSWEIGHT 8 Residual CubesTOLERANCE 8 Residual MapsDENSNVZ0 00MNMX 8 Values for Peaks in CubesVDISP 8 Values for Peaks in Maps
  • 30. AMIGA4GAS AMIGA for the GTC, ALMA, and SKA PathfindersTechnical part, devoted to computing & datafederation Heterogeneous computing federation IN PARTNERSHIP WITH Local computing cluster, grid, cloud computing BSC, FCSCLMain Goals Porting the Taverna workflow engine to supercomputing environments Development of an integration layer for automatic workflow deployment DIRECT RELEVANCE TO SKA SCIENCE DATA PROCESSOR
  • 31. Infrastructure Federation FED4AMIGA GRID RESOURCE MANAGER SUPER COMPUTER COMPSs CLOUDFED4AMIGA: FEDERATION OF INFRASTRUCTURES• HOW TO INTEGRATE THE INFRASTRUCTURES IN A FEDERATED SYSTEM?• HOW TO AUTHENTICATE THE USERS?• HOW TO IMPLEMENT BUSINESS RULES TO DECIDE IN WHICH INFRASTRUCTURE THE TASK SHOULD RUN?
  • 32. Wf4Ever + CyberSKAWorkflows can be used to formally specifyprocessing tasks Specially good when computing tasks exists as services Even better if data can be referenced, instead of sent over the wire
  • 33. Wf4Ever + CyberSKAComplementary to CyberSKA (infrastructure)Wf4Ever places more emphasis on End-user tools VERY MUCH process creation SCIENCE-ORIENTED data manipulation annotation interdisciplinary algorithm repurposing Long-term Preservation, Quality Assurance
  • 34. SKA ComputingSynergies
  • 35. SKA ComputingSynergiesThe SKA computing power amounts to being ableto sift through the entire Internet more than100 times per day Citizens can be empowered, through SKA-like tools, to process city wide, regional, or national data for insightIntelligent sensor networks can provide tools forbetter, instantaneous, resource planning IN LINE WITH H2020 PRIORITIES
  • 36. Conclusions
  • 37. ConclusionsThe SKA is the proverbial e-Science instrumentWorkflows can be used for both machine-readable, formal process description, and human-readable scientific tool developmentFederated, transparent workflow computingWf4Ever & AMIGA4GAS are complementary toCyberSKA, SKA.TEL.SDP work
  • 38. ConclusionsIt is a long road towards the SKA, but we have toget involved in this problems now
  • 39. Thank you! Julián Garrido José Enrique Ruiz del Mazo Susana Sánchez Expósito Juan de Dios Santander-Vela <jdsant@iaa.es> Lourdes Verdes-Montenegro

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