1. Pegasus: Running Large-Scale Scientific Workflows on the TeraGrid Ewa Deelman USC Information Sciences Institute http://pegasus.isi.eduhttp://pegasus.isi.edu www.isi.edu/~deelmanwww.isi.edu/~deelman

2. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Acknowledgements Carl Kesselman, Gaurang Mehta, Gurmeet Singh, Mei-Hui Su, Karan Vahi (Center for Grid Technologies, ISI) James Blythe, Yolanda Gil (Intelligent Systems Division, ISI) http://pegasus.isi.edu Research funded as part of the NSF GriPhyN, NVO and SCEC projects, NIH-funded CRCNS project and EU-funded GridLab Thanks for the use of the TeraGrid

3. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Outline Applications as workflows Pegasus (Planning for Execution in Grids) Montage application (Astronomy, NSF&NASA) CyberShake (Southern California Earthquake Center) Results from running on the TeraGrid Conclusions

4. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Today’s Scientific Applications Applications Increasing in the level of complexity Use of individual application components Components are supplied by various individuals Reuse of individual intermediate data products (files) Execution environment is complex and very dynamic Resources come and go Data is replicated Components can be found at various locations or staged in on demand Separation between the application description the actual execution description Applications being described in terms of workflows

5. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu

7. Executable Workflow Generation and Mapping WINGS and CAT, developed at ISI by Y. Gil, VDL, developed at ANL & Uof C by I. Foster, J. Voeckler & M. Wilde

8. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Pegasus: Planning for Execution in Grids Maps from abstract to executable workflow Automatically locates physical locations for both workflow components and data Finds appropriate resources to execute the components Reuses existing data products where applicable Publishes newly derived data products Provides provenance information

9. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Information Components used by Pegasus Globus Monitoring and Discovery Service (MDS) (or static file) Locates available resources Finds resource properties Dynamic: load, queue length Static: location of GridFTP server, RLS, etc Globus Replica Location Service Locates data that may be replicated Registers new data products Transformation Catalog Locates installed executables

10. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Example Workflow Reduction Original abstract workflow If “b” already exists (as determined by query to the RLS), the workflow can be reduced Also useful in case of failures

11. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Mapping from abstract to executable Query RLS, MDS, and TC, schedule computation and data movement

12. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Mosaic of M42 created on the Teragrid resources using Pegasus Pegasus improved the runtime of this application by 90% over the baseline case Workflow with 4,500 nodes Bruce Berriman, John Good (Caltech) Joe Jacob, Dan Katz (JPL) Gurmeet Singh, Mei Su (ISI)

13. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Small Montage Workflow ~1200 nodes

14. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Montage Initial prototype implemented and tested on the TeraGrid Montage performance evaluations Production Montage portal open to the astronomy community this year Collaboration with JPL & IPAC

15. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu SCEC Derive Probabilistic Hazard Curves & maps for the Los Angeles Area: 6 sites in 2005, 625 in 2006, and 10,000 in 2007 Probability of a certain ground motion during a certain period of time Hazard Map

16. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu SCEC workflows on the TG Executable workflow

17. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu SCEC Workflows on the TG Gaurang Mehta at ISI ran the experiments Local machine

18. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu SCEC computations so far Pasadena 33 workflows USC 26 workflows Each workflow [11, 1000] jobs 23 days total runtime NCSA & SDSC TG Failed job recovery Retries Rescue DAG

19. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu So far 2 SCEC sites done (Pasadena and USC)

20. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Distribution of seismogram jobs 70 hours

21. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Observations from working with the Scientists Two way street: they give us feedback on our technologies, we show them how things run (break) at scale We have seen great performance improvements in the codes

22. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Some other Pegasus Application Domains Laser Gravitational Wave Observatory (LIGO) Galaxy morphology (NVO) Tomography for neural structure reconstruction (NIH) High-energy physics Gene alignment Natural Language processing

23. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu LIGO has used Pegasus to run on the Open Science Grid at SC’05 Courtesy of David Meyers, Caltech

24. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Benefits of the workflow & Pegasus approach Pegasus can run the workflow on a variety of resources Pegasus can run a single workflow across multiple resources Pegasus can opportunistically take advantage of available resources (through dynamic workflow mapping) Pegasus can take advantage of pre-existing intermediate data products Pegasus can improve the performance of the application.

25. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Benefits of the workflow & Pegasus approach Pegasus shields from the Grid details The workflow exposes the structure of the application maximum parallelism of the application Pegasus can take advantage of the structure to Set a planning horizon (how far into the workflow to plan) Cluster a set of workflow nodes to be executed as one (for performance)

26. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Pegasus Research resource discovery and assessment resource selection resource provisioning workflow restructuring task merged together or reordered to improve overall performance adaptive computing Workflow refinement adapts to changing execution environment workflow debugging

27. Ewa Deelman, deelman@isi.eduwww.isi.edu/~deelmanpegasus.isi.edu Software releases Pegasus http://pegasus.isi.eduhttp://pegasus.isi.edu released as part of the GriPhyN Virtual Data System (VDS) Collaborators in VDS: Ian Foster (ANL) Mike Wilde (ANL) and Jens Voeckler (Uof C) http://vds.isi.edu