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Virtual Data and the Chimera System*

Virtual Data and the Chimera System*. Ian Foster Mathematics and Computer Science Division Argonne National Laboratory and Department of Computer Science The University of Chicago http://www.mcs.anl.gov/~foster *Joint work with Jens Vöckler , Mike Wilde, Yong Zhao.

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Virtual Data and the Chimera System*

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  1. Virtual Dataand the Chimera System* Ian Foster Mathematics and Computer Science Division Argonne National Laboratory and Department of Computer Science The University of Chicago http://www.mcs.anl.gov/~foster *Joint work with Jens Vöckler, Mike Wilde, Yong Zhao HPC 2002 Conference, Cetraro, June 26, 2002

  2. Overview • Problem • Managing programs and computations as community resources • Technology • Chimera virtual data system • Applications • Virtual Data ≠ Virtual Concept! • Futures • Research challenges & plans

  3. Overview • Problem • Managing programs and computations as community resources • Technology • Chimera virtual data system • Applications • Virtual Data ≠ Virtual Concept! • Futures • Research challenges & plans

  4. Programs as Community Resources:Data Derivation and Provenance • Most [scientific] data are not simple “measurements”; essentially all are: • Computationally corrected/reconstructed • And/or produced by numerical simulation • And thus, as data and computers become ever larger and more expensive: • Programs are significant community resources • So are the executions of those programs • Management of the transformations that map between datasets an important problem

  5. “I’ve come across some interesting data, but I need to understand the nature of the corrections applied when it was constructed before I can trust it for my purposes.” Motivations (1) “I’ve detected a calibration error in an instrument and want to know which derived data to recompute.” Data consumed-by/ generated-by created-by Transformation Derivation execution-of “I want to apply an astronomical analysis program to millions of objects. If the results already exist, I’ll save weeks of computation.” “I want to search an astronomical database for galaxies with certain characteristics. If a program that performs this analysis exists, I won’t have to write one from scratch.”

  6. Motivations (2) • Data track-ability and result audit-ability • Universally sought by GriPhyN applications • Repair and correction of data • Rebuild data products—c.f., “make” • Workflow management • A new, structured paradigm for organizing, locating, specifying, and requesting data products • Performance optimizations • Ability to re-create data rather than move it • And others, some we haven’t thought of

  7. Overview • Problem • Managing programs and computations as community resources • Technology • Chimera virtual data system • Applications • Virtual Data ≠ Virtual Concept! • Futures • Research challenges & plans

  8. GriPhyN VDT: Replica catalog DAGMan Globus Toolkit Etc. Chimera Virtual Data System • Virtual data catalog • Transformations, derivations, data • Virtual data language • VDC definition and query • Applications include browsers and data analysis applications

  9. Transformations and Derivations • Transformation • Abstract template of program invocation • Similar to "function definition" in C • Derivation • Formal invocation of a Transformation • Similar to "function call" in C • Store past and future: • A record of how data products were generated • A recipe of how data products can be generated • Invocation (future) • Record of each Derivation (re) execution • Similar to strace (BSD) or truss (SysV)

  10. Virtual Data Catalog Structure

  11. Virtual Data Tools • Virtual Data API • A Java class hierarchy to represent transformations and derivations • Virtual Data Language • Textual for people & illustrative examples • XML for machine-to-machine interfaces • Virtual Data Database • Makes the objects of a virtual data definition persistent • Virtual Data Service • Provides a service interface (e.g., OGSA) to persistent objects

  12. Virtual Data Language: XML

  13. Example Transformation TR t1( out a2, in a1, none pa = "500", none env = "100000" ) { profile hints.exec-pfn = "/usr/bin/app3"; argument = "-p "${pa}; argument = "-f "${a1}; argument = "-x –y"; argument stdout = ${a2}; profile env.MAXMEM = ${env}; } $a1 t1 $a2

  14. Example Derivations DV d1->t1 (env="20000", pa="600",a2=@{out:run1.exp15.T1932.summary},a1=@{in:run1.exp15.T1932.raw}, ); DV d2->t1 (a1=@{in:run1.exp16.T1918.raw},a2=@{out.run1.exp16.T1918.summary} );

  15. Managing Dependencies file1 TR tr1( out a2, in a1 ) { profile hints.exec-pfn = "/usr/bin/app1";  argument stdin = ${a1};  argument stdout = ${a2}; } TR tr2( out a2, in a1 ) { profile hints.exec-pfn = "/usr/bin/app2"; argument stdin = ${a1}; argument stdout = ${a2}; } DV x1->tr1( a2=@{out:file2}, a1=@{in:file1}); DV x2->tr2( a2=@{out:file3}, a1=@{in:file2}); x1 file2 x2 file3

  16. Initial “Strawman” Architecture(Use of GriPhyN Virtual Data Toolkit) VDLx abstract planner DAX concrete planner DAGMan

  17. Overview • Problem • Managing programs and computations as community resources • Technology • Chimera virtual data system • Applications • Virtual Data ≠ Virtual Concept! • Futures • Research challenges & plans

  18. Galaxy cluster size distribution Chimera Virtual Data System + GriPhyN Virtual Data Toolkit + iVDGL Data Grid (many CPUs) Chimera Application:Sloan Digital Sky Survey Analysis Size distribution of galaxy clusters? Joint work with Jim Annis, Steve Kent, FNAL

  19. catalog tsObj core core brg field tsObj tsObj cluster field brg field tsObj brg brg field 5 4 3 2 1 2 1 2 1 1 2 3 Cluster-finding Data Pipeline

  20. Cluster-Finding Pipeline Execution

  21. Small SDSS Cluster-Finding DAG

  22. And Even Bigger:744 Files, 387 Nodes 50 60 168 108

  23. Overview • Problem • Managing programs and computations as community resources • Technology • Chimera virtual data system • Applications • Virtual Data ≠ Virtual Concept! • Futures • Research challenges & plans

  24. Virtual Data Usage Model • Transformation designers create programmatic abstractions • Simple or compound; augment with metadata • Production managers create bulk derivations • Can materialize data products or leave virtual • Users track their work through derivations • Augment (replace?) the scientist’s log book • Definitions can be augmented with metadata • The key to intelligent data retrieval • Issues relating to metadata propagation

  25. Virtual Data Research Issues • Representation • Metadata: how is it created, stored, propagated? • What knowledge must be represented? How? • Capturing notions of data approximation • Higher-order knowledge: virtual transformations • VDC as a community resource • Automating data capture • Access control and privacy issues • Quality control • Data derivation • Query estimation and request planning

  26. Virtual Data Research Issues • “Engineering” issues • Dynamic (runtime-computed) dependencies • Large dependent sets • Extensions to other data models: relational, OO • Virtual data browsers • XML vs. relational databases & query languages • Additional usage modalities • E.g., meta-analyses, automated experiment generation, “active notebooks” • Virtual data browsers, editors

  27. Status of Chimera R&D • Early virtual data system demonstrated Nov ’01: HEP collision simulations • Larger scale problems addressed recently: “cluster finding” in SDSS • First public release in June: Chimera v1.0 • Enhancements planned throughout the summer • Physics & astronomy applications by SC’02 • Future R&D focus #1: request planning • Future R&D focus #2: knowledge representation • Future apps: bioinformatics, earth sciences

  28. Related Work • Data provenance • Materialized views, lineage: Cui, Widom • Data provenance tracking: Buneman et al. • Capturing transformations • ZOO system and conceptual schema • Data Grid technologies • GriPhyN, Globus Project, EU DataGrid

  29. Summary • Concept: Tools to support management of transformations and derivations as community resources • Technology: Chimera virtual data system including virtual data catalog and virtual data language; use of GriPhyN virtual data toolkit for automated data derivation • Results: Successful early applications to CMS and SDSS data generation/analysis • Future: Public release of prototype, new apps, knowledge representation, planning

  30. For More Information • GriPhyN project (NSF ITR funded) • www.griphyn.org • Chimera virtual data system • www.griphyn.org/chimera • “Chimera: A Virtual Data System for Representing, Querying, and Automating Data Derivation,” SSDBM, July 2002 • “Applying Chimera Virtual Data Concepts to Cluster Finding in the Sloan Sky Survey”, SC’02, November 2002.

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