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Kepler Project Overview, Status, Future Directions

Kepler Project Overview, Status, Future Directions. Bertram Ludäscher University of California, Davis. Overview. History Origins, Diversity, Challenges Kepler, Kepler/CORE: Issues, Status Next steps Research: Scientific Workflows … Business (workflows) as usual? Or… ?.

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Kepler Project Overview, Status, Future Directions

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  1. Kepler Project Overview, Status, Future Directions Bertram Ludäscher University of California, Davis

  2. Overview • History • Origins, Diversity, Challenges • Kepler, Kepler/CORE: • Issues, Status • Next steps • Research: Scientific Workflows … • Business (workflows) as usual? • Or… ?

  3. Kepler: Some History • The Origins: • AD 2002: NSF/SEEK, DOE/SDM • Similar requirements for “scientific workflows” • Can we avoid reinventing the wheel (twice…) !? • Grass-roots effort, open source collaboration • The Head-start: • Adopting, extending Ptolemy II from Berkeley • Common software platform facilitates grass-root collaboration • More than software: Research Results • Heterogeneous Modeling & Design, dataflow-, actor-oriented MoCs

  4. Upperware Upper Middleware Middleware Underware Scientific Workflows: Cyberinfrastructure “Upperware” NSF/SEEK ITR, 5 Year collaboration: SDSC, UCSB, UCD, UNM, UK, …

  5. Scientific Workflow Capture how a scientist works with data and analytical tools • data access, transformation, analysis, visualization • possible worldview: dataflow-oriented (cf. signal-processing) Scientific workflow (wf) benefits (compare w/ script-based approaches) : • wf automation • wf & component reuse • wf design, documentation • wf archival, sharing • built-in concurrency (task-, pipeline-parallelism) • built-in provenance support • distributed & parallel exec: Grid & cluster support • …

  6. Data source from EcoGrid (metadata-driven ingestion) R processing script res <- lm(BARO ~ T_AIR) res plot(T_AIR, BARO) abline(res) Simple Kepler analysis workflow using R … Dan Higgins, NCEAS

  7. to monitor, control remote supercomputer simulations … 50+ composite actors (subworkflows) 4 levels of hierarchy 1000+ atomic (Java) actors 43 actors, 3 levels 196 actors, 4 levels 30 actors 206 actors, 4 levels 33 actors 137 actors 123 actors 150 66 actors 12 actors 243 actors, 4 levels … vs. “Plumbing” workflow Norbert Podhorszki Then: UC Davis, now: ORNL …

  8. Kepler: Open Source + Open Community • Huge diversity of domains => needs • Astrophysics, nuclear fusion research, geoinformatics, ecology, systematics, bioinformatics, genomics, environmental monitoring, simulation, … • Not just bioinformatics and cheminformatics … • A broad range of technical problems • Workflowdesign with a graphical UI • Sharing actors, workflows across communities • Distributed workflow execution • Data movement on the network • Integrate local apps, web services, native actors • Support a variety of computational models • Not just web service orchestration or Grid deployment

  9. Kepler: Open Source + Open Community 3. Many kinds of users with different backgrounds and responsibilities: • Scientistsautomating and sharing their analyses of their own data or performing meta-analyses on others’ data • Software engineersdeveloping their own systems around Kepler • Computer scientists doing basic research in scientific workflows, data and provenance management, distributed and collaborative computing. • Not just biologists and chemists … 4. Kepler used in many different deployment contexts • Standalone application on a scientist’s desktop computer or laptop. • Backend for web-based scientific applications. • Embedded workflow engine in larger systems. • One size (of deployment) does not fit all! 5. Kepler open to contribution and extension by anyone: • Anyone can contribute to Kepler! • Anyone canuse Kepler in their own applications • Developing with Kepler doesn’t require collaboration with the “owners” …

  10. Kepler CORE Kepler CORE COMET! * * * * ChIP-chip Phylogenetics Astronomy Library Science Ecology Conservation Biology Oceanography Geosciences Molecular Biology Chemistry Particle Physics

  11. Kepler-CORE Mission In collaboration with current and future contributors to Kepler, the Kepler/CORE team will … • Develop and maintain the essential, interdisciplinary software components of Kepler • Coordinate the contributions of the greater Kepler collaboration to the core (“kernel”) of the system • Increase the role of the current and future user community in specifying requirements and priorities

  12. The Kepler-CORE Project and Team • Kepler-CORE(sensu stricto) • 3-year, $1.7M NSF-OCI funded project • Kepler-CORE team @ UCD,UCSB, UCSD: • UC Davis • Bertram Ludäscher (PI@UCD), Shawn Bowers (co-PI), Tim McPhillips (co-PI & software architect), David Welker (software engineer), Sean Riddle (software engineer) • UC Santa Barbara • Matthew Jones (PI@UCSB), Mark Schildhauer (co-PI), Aaron Schultz, Chad Berkley (software engineer) • UC San Diego • Ilkay Altintas (PI@SDSC), Jianwu Wang (postdoc) • Kepler/CORE(sensu lato) • Goal: sustain long-term, beyond initial funding period • KEPLER = Kepler/Core + Kepler/X + Kepler/Y + … • Core, X, Y, … = open community of stakeholders, contributors, users, etc.

  13. Kepler-CORE Duo Kepler-CORE Vision In the future we foresee Kepler… • Satisfying the scientific workflow automation needs of • Collaborative government-funded projects • Academic research groups • Individual researchers in diverse scientific disciplines • Enhancing the productivity of researchers by • Facilitating discovery and collaboration within and across disciplines • Being the best way for scientists to leverage developments and expertise in other domains • Leading to further breakthroughs and innovations in the fields of • Scientific data management • Data provenance • Collaborative scientific computing • Shepherded by a self-sustaining effort that thrives well beyond the lifetimes of the grants that have contributed to Kepler’s development.

  14. These differences mean that the Kepler collaboration will be unique, too • Kepler cannot solve everyone’s problems right out of the box • Kepler must be adaptable to different domain sciences • Adaptation requires more than developing new actors • Kepler is as much a development platform as an “end-user” tool • No one group can take responsibility for supporting all the ways Kepler will be used … • Kepler is open-source but more complex than other open source projects • Diversity of domains, users, and deployment contexts mean there can be conflicts between the needs or priorities of contributors • Need a way of developing and adding extensions without breaking other’s systems • Software engineers developing code for Kepler often are not expert scientists, and cannot be the final authority on what the system should do (unlike projects like Apache, Linux, etc where the engineers are the expert users themselves and can add what they need) • PIs and project managers on projects extending Kepler must take responsibility for knowing what needs to be done. • It is essential that for each project employing Kepler, representatives authoritative on the scientific and technical needs of their projects participate in driving the future development of Kepler!

  15. Stakeholders: Essential to Success of Kepler Kepler stakeholders … • Are projects and individuals whose work depend critically on the success of Kepler. • Are funded by a variety of sources and work in diverse fields of scientific research. • Are more likely to greatly extend Kepler and use Kepler within their own systems than simply develop packages of actors and workflows for use with a standard distribution of Kepler. • Need to deliver the software systems they develop to their own community of users. • Must deliver their software systems according to their own (e.g. release) schedules as determined by their research and funding programs. • Have different requirements that will conflict in the absence of mechanisms for enabling independent extension and deployment of Kepler-based systems. • Require recognition for the contributions they make to Kepler as well as for their own systems based on Kepler. • Know better than us what they need from Kepler.

  16. Kepler(-CORE) Management • Leadership Team • 3 year terms (current members from UC + [S] + {B, D} ) • Focus on • long term viability of Kepler • strategic decisions on behalf of user community, Kepler project • Interest Groups • Communicate, collaborate on specialized capabilities • Development Teams • Design, develop, test specific software deliverables • Infrastructure Teams • Identify, discuss, design, implement Kepler Framework

  17. New Kepler Build System • Modules and suites • Develop against trunk or specific version, release • Tag, branch Kepler extensions independently of the kernel • easier to share develop, share extensions • svn repository (https://code.kepler-project.org/code/kepler/) • Module Manager • New component to simplify working with modules David Welker et al.

  18. Kepler Release Roadmap • https://kepler-project.org/developers/teams/build/kepler-release-roadmap • Kepler releases • based on a “standard set” of modules • Individual module releases • Provenance • Workflow reporting • COMAD • Distributed: Master/Slave • … • Kepler 2.0 • Add modules, extensions to installed Kepler dynamically • Targeted for Summer 2009

  19. New Plone-based Web site, Forums

  20. Kepler/REAP: Workflow Run Manager Derik Barseghian et al. • Use case “Publication-Ready Archive” • Archive workflow with inputs, outputs • Tagging • Also: Outline view • to manage browsing of large/deeply nested workflows

  21. Workflow Reports (Provenance Interest Group) Derik Barseghian et al.

  22. Kepler and Scientific Workflow Research • Scientific Workflows: • Business (workflows) as usual? • Data-oriented, data-centric … • … as opposed to control-, task-centric • Signal processing? • Or else …? • Modeling scientific processes, analysis methods • Understanding is in the Mind of the Beholder! • Example areas: • Workflow Modeling & Design • Provenance • Optimization

  23. Modeling Example (ChIP-chip workflow) Tim McPhillips et al.

  24. Vanilla Process Network Functional Programming Dataflow Network XML Transformation Network Collection-oriented Modeling & Design framework (COMAD) “Look Ma: No Shims!” Modeling & Design: The limits of my language mean the limits of my world …

  25. Two different workflow designs

  26. AXG AYG AZG RI1 AI1 alignWarp:1 reslice:1 AH1 convert:1 WP1 slicer:1 RH1 AXG AXS RI2 AI2 alignWarp:2 reslice:2 AH2 AI WP2 softmean:1 slicer:2 RH2 convert:2 RI RH RI4 AYG AYS AH alignWarp:3 reslice:3 AI4 WP4 AH4 RH4 slicer:3 convert:3 RI4 AZG reslice:4 alignWarp:4 AZS AI4 WP4 AH4 RH4 outputs inputs Data Provenance • Keep track of data dependencies, processing history  support interpretation, validation, reproducibility AlignWarp Reslice Softmean Slicer Convert

  27. Kepler/pPOD: Provenance Browser Shawn Bowers et al. For conventional data provenance and fine-grained dependencies (COMAD style) Navigate forward and backward in time (VCR style) in different views (collections, processes, combined)

  28. Collection History • Collection and invocation view • Incrementally step through execution history

  29. From MoCs to Models of Provenance (MoPs)

  30. Fine-grained, Data & MoC-aware MoP Manish Anand, Shawn Bowers, et al.

  31. Optimization: Multi-level Workflows Kepler LPPN Daniel Zinn et al.

  32. Modeling + Optimization: Virtual Assembly Lines (COMAD)

  33. Layers in COMAD / ∆-XML Pipelines WF Graph • Access data in XML stream • Call Scientific Functions (Services) • Put results back into stream Configurations (white-box) CipresRAxML Scientific Functions (black-boxes) Out: (t:Tree, s:score)+ In: DNASeq+ Thres: Float Method: String Daniel Zinn (UC Davis)

  34. Conventional vs Assembly Line / COMAD Thinking Daniel Zinn (UC Davis)

  35. More secret sauce: User vs. Optimized Dataflow

  36. Conceptual Pipeline w/ Scopes & Types Daniel Zinn (UC Davis)

  37. X-CSR (“XML Scissor”): Cut-Ship-Reassemble Daniel Zinn (UC Davis)

  38. UC DAVIS Department of Computer Science Acknowledgments • Kepler contributors • Many individuals: https://kepler-project.org/developers/kepler-contributors • Projects: Ptolemy, SEEK, SDM, CPES, GEON, REAP, CIPRes, ChIP2, pPOD, COMET, BAP, LTER, RAPR, ITER, … • Funding agencies: NSF, DOE, … • DAKS @ UC Davis Members • Research Staff • Drs. Shawn Bowers, Timothy McPhillips, Lei Dou, Ustun Yildiz • Developers: • Sean Riddle, David Welker, Gongjing Cao • Students: • Manish Anand, Dave Thau, Daniel Zinn, Sven Koehler, Saumen Dey, Supriya Gulati, Faraaz Sareshwala, Xuan Li

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