GriPhyN: Grid Physics Network and iVDGL: International Virtual Data Grid Laboratory

1. GriPhyN: Grid Physics NetworkandiVDGL: International Virtual Data Grid Laboratory

2. Collaboratory Basics • Two NSF-funded Grid projects in HENP (high energy and nuclear physics) and computer science • MPS and CISE have oversight • GriPhyN and iVDGL are too closely related to discuss one without discussing the other • One is CS research and test application, the other is to build an international scale facility to do these tests, and to address other goals as well • Share vision, personnel and components • These two collaboratories are part of a larger effort to develop the components and infrastructure for supporting data intensive science

3. Some Science Drivers • Computation is becoming an increasingly important tool of scientific discovery • Computationally intense analyses • Global collaborations • Large datasets • The increasing importance of computation in science is more pronounced in some fields • Complex (e.g. climate modeling) and high volume (HEP) simulations • Detailed rendering (e.g. biomedical informatics) • Data intensive science (e.g. astronomy and physics) • GriPhyN and iVDGL were founded to provide the modelsand software for the data management infrastructure for four large projects

4. SDSS / NVO • SDSS / NVO are in full production • Explore how the Grid can be used in astronomy • What’s the benefit? • How to integrate? • How can the Grid be used for future sky surveys? • Data processing pipelines are complex • Has made the most sophisticated use of the virtual data concept

5. LIGO • Not in full production, but real data is being taken • LIGO I Engineering Runs • 35 TB since 1999 and growing • LIGO I Science Runs • 62 TB in two science runs, additional run planned that will generate 135 TB • Eventual constant operation at 270 TB/year • LIGO II Upgrade • Eventual Operation at 1-2 PB / year • Need distributed computing power of the Grid • Need virtual data catalogs for efficient dissemination of data and management of workflow

6. CMS / ATLAS • CMS and ATLAS are two experiments being developed for the Large Hadron Collider at CERN • Two projects, two cultures, but: • Similar data challenges • Similar geographic distribution • Moving closer to common tools through the LCG computing grid. • Petabytes of data per year (100 PB by 2012)

7. Function Types • GriPhyN • Distributed Research Center • iVDGL • Community Data System

8. GriPhyN

9. GriPhyN Funding • Funded in 2000 through NSF ITR program • $11.9M + $1.6M matching

10. GriPhyN Project Team • Led by U. Florida and U. Chicago • PD’s Paul Avery (UF) and Ian Foster (UC) • 22 Participant institutions • 13 funded • 9 unfunded • Roughly 82 people involved • 2/3 of activity computer science, 1/3 physics

11. Funded Institutions U. Florida U. Chicago CalTech U. Wisconsin - Madison USC / ISI Indiana U. Johns Hopkins U. Texas A & M UT Brownsville UC Berkeley U Wisconsin Milwaukee SDSC Unfunded Institutions Argonne NL Fermi NAL Brookhaven NL UC San Diego U. Pennsylvania U. Illinois - Chicago Stanford Harvard Boston U. Lawrence Berkeley Lab

12. Technology • GriPhyN’s science drivers demand timely access to very large datasets and the computer cycles and information management infrastructure needed to manipulate and transform those datasets in a meaningful way • Data Grids are an approach to data management and resource sharing in environments where datasets are very large • Policy-driven resource sharing, distributed storage, distributed computation, replication and provenance tracking • GriPhyN and iVDGL aim to enable petascale virtual data grids

13. Petascale Virtual DataGrids Production Team Single Researcher Workgroups Interactive User Tools Request Execution & Management Tools Request Planning &Scheduling Tools Virtual Data Tools ResourceManagementServices Security andPolicyServices Other GridServices • PetaOps • Petabytes • Performance Transforms Distributed resources(code, storage, CPUs,networks) Raw datasource

14. GriPhyN Datagrid Contributions • GriPhyN has three areas of contribution for achieving the DataGrid vision • Contributing CS research • Virtual Data as a unifying concept • Planning, execution and performance monitoring • Integrating these facilities in a transparent and high-productivity manner: Making the grid as easy to use as a workstation and the web • Disseminating this research through the Virtual Data Toolkit and other tools • Chimera • Pegasus • Integrate CS research results into GriPhyN science projects • GriPhyN experiments serve as an exciting but demanding CS and HCI laboratory

15. Virtual Data Toolkit: VDT • A suite of tools developed by the CS team to support science on the Grid • Uniting theme is virtual data • Nearly all data in physics / astronomy is virtual data - derivations of a large, well known data set • It is possible to represent derived data as the set of instructions that created it • There is no need to always copy a derived data set - it can be recomputed if you have the workflow • Virtual data also has a number of beneficial side effects, e.g. data provenance,discovery, re-creation, workflow automation • Many packages, a few are unique to GriPhyN, others are common across many Grid projects

16. “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 “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.” Slide courtesy Ian Foster

17. Chimera • The Chimera Virtual Data System is one of the core tools of the GriPhyN Virtual Data Toolkit • Virtual Data Catalog • Represents transformationprocedures and derived data • Virtual Data Language Interpreter • Translates user requests into Grid workflow

18. Pegasus • Planning Execution in Grids • Tool for mapping complex workflows onto the Grid • Converts abstract Chimera workflow into a concrete workflow, which is sent to DAGman for execution • DAGman is the Condor meta-scheduler • Determines sites and data transfers

19. Virtual Data Processing Tools VDLx abstract planner XML DAG Pegasus concrete planner Local shell planner Condor DAG shell DAG

20. Example:Sloan Galaxy Cluster Finder DAG Sloan Data Galaxy cluster size distribution Jim Annis, Steve Kent, Vijay Sehkri, Fermilab, Michael Milligan, Yong Zhao, Chicago

21. Example:Sloan Galaxy Cluster DAG Sloan Data Galaxy cluster size distribution With Jim Annis & Steve Kent, FNAL

22. Resource Diagram

23. International Virtual Data Grid Laboratory: iVDGL

24. Some Context • There is much more to the DataGrid world than GriPhyN • Broad problem space, with many cooperative projects • U.S. • Particle Physics Data Grid (PPDG) • GriPhyN • Europe • DataTAG • EU DataGrid • International • iVDGL

25. Background and Goals • U.S. portion funded in 2001 as a Large ITR through NSF • $13.7M + $2M matching • International partners responsible for own funding • Aims of iVDGL • Establish a Global Grid Laboratory • Conduct DataGrid tests at scale • Promote interoperability • Promote testbeds for non-physics applications

26. Relationship to GriPhyN • Significant overlap • Common management, personnel overlap • Roughly 80 people on each project, 120 total • Tight technical coordination • VDT • Outreach • Testbeds • Common External Advisory Committee • Different focus - domain challenges • GriPhyN - 2/3 CS, 1/3 Physics: IT Research • iVDGL - 1/3 CS, 2/3 Physics: Testbed deployment and operation

27. Project Composition • CS Research • U.S. iVDGL Institutions • UK e-science programme • DataTAG • EU DataGrid • Testbeds • ATLAS / CMS • LIGO • National Virtual Observatory • SDSS

28. iVDGL Institutions U Florida CMS Caltech CMS, LIGO UC San Diego CMS, CS Indiana U ATLAS, iGOC Boston U ATLAS U Wisconsin, Milwaukee LIGO Penn State LIGO Johns Hopkins SDSS, NVO U Chicago CS U Southern California CS U Wisconsin, Madison CS Salish Kootenai Outreach, LIGO Hampton U Outreach, ATLAS U Texas, Brownsville Outreach, LIGO Fermilab CMS, SDSS, NVO Brookhaven ATLAS Argonne Lab ATLAS, CS T2 / Software CS support T3 / Outreach T1 / Labs(not funded)

29. SKC Boston U Wisconsin Michigan PSU BNL Fermilab LBL Argonne J. Hopkins NCSA Indiana Hampton Caltech Oklahoma Vanderbilt UCSD/SDSC FSU Arlington UF Tier1 FIU Tier2 Brownsville Tier3 US-iVDGL Sites Partners? • EU • CERN • Brazil • Australia • Korea • Japan

30. Component Projects • iVDGL contains several core projects • iGOC • International Grid Operations Center • GLUE • Grid Laboratory Uniform Environment • WorldGrid – 2002 international demo • Grid3 – 2003 deployment effort

31. iGOC • International Grid Operations Center • iVDGL “headquarters” • Analogous to a Network Operations Center • Located at Indiana University • Single point of contact for iVDGL operations • Database of contact information • Centralized information about storage, network and compute resources • Directory for monitoring services at iVDGL sites

32. GLUE • Grid Laboratory Uniform Environment • A grid interface subset specification that permits applications to run on grids from VDT and EDG sources • Effort to ensure interoperability across numerous physics grid projects • GriPhyN, iVDGL, PPDG • EU DataGrid, DataTAG, CrossGrid, etc. • Interoperability effort focuses on: • Software • Configuration • Documentation • Test suites

33. WorldGrid • Effort at a world wide DataGrid • Easy to deploy and administer • Middleware based on VDT • Chimera development • Scalability • Demo at SC2002 • United DataTAG and iVDGL

34. Resource Diagram

35. U.S. Piece US ProjectDirectors International Piece US External Advisory Committee Collaborating Grid Projects GriPhyNMike Wilde US Project Steering Group DataTAG TeraGrid EDG LCG? Asia BTEV ALICE Bio Geo ? Facilities Team D0 PDC CMS HI ? Core Software Team Operations Team Project Coordination Group Applications Team GLUE Interoperability Team Outreach Team iVDGL Management

36. Issues across projects • Technical readiness • Infrastructure readiness • Collaboration readiness • Common ground • Coupling of tasks • Incentives

37. Technical readiness • Very high • Physics and CS are both very high on the adoption curve, generally • Long history of infrastructure development to support national and global experiments

38. Infrastructure readiness • Also quite high • Not all of the pieces are in place to meet demand • The expertise exists within these communities to build and maintain the necessary infrastructure • Community is inventing the infrastructure • Real understanding in the project that interoperability and standards are part of infrastructure

39. Collaboration readiness • Again, quite high • Physicists have a long history of large scale collaboration • CS collaborations built on old relationships with long time collaborators

40. Common ground • Perhaps a bit too high • What you can do with a physics background: • Win the ACM Turing Award • Co-invent the World Wide Web • Direct the development of the Abilene backbone • Because application community has a strong understanding of the required work and the technical aspects of the work, some friction about how work separates • History of physicists building computational tools e.g. ROOT

41. Coupling of tasks • Tasks decompose into subtasks that are somewhat tightly coupled • Locate tightly coupled tasks at individual sites

42. Incentives • Both groups are well motivated, but for different reasons • CS is engaging in extremely cutting edge research across a large range of activities • Funded for deployment as well as development • Physics is structurally committed to global collaborations

43. Some successes • Lessons in infrastructure development • Outreach and engagement • Community buy-in / investment • Achieving the CS research goals for Virtual Data and Grid execution

44. Infrastructure Dev • Looking at the history of the Grid (electrical, not computational) • Long phases • Invention • Initial production use • Adaptation • Standardization / regulation • Geographically bounded dominant design • I.e. 220 vs. 110

45. Infrastructure Dev • We don’t see this with GriPhyN / iVDGL • Projects concurrent, not consecutive • Pipeline approach to phases of infrastructure development • Real efforts at cooperation with other DataGrid communities • Why? • Deep understanding at high levels of project that building it alone is not enough • Directive and funding from NSF to do deployment

46. Outreach • The GriPhyN / iVDGL community is extremely active in outreach to other projects and communities • Evangelizing virtual data • Distributed tools • This is a huge win for building CI that others can use

47. Community buy-in • Together, these projects are funded at nearly $30M over 6 years • This does not represent the total investment that was needed to make this work • Leveraged FTE • Unfunded testbed sites • International partners • Lots of collaboration with PPDG; starting some with Alliance Expeditions, etc • This kind of community commitment necessary for a project of this size to succeed

48. Challenges • Staying relevant • Building infrastructure with term limited funding

49. Staying Relevant (1) • The application communities are fast paced, high power groups of people • Real danger in those communities developing tools that satisfice while they wait for the tools that are optimal and fit into a greater cyberinfrastructure • Each experiment ideally wants tools perfectly tailored to their needs • Maintaining user engagement and meeting the needs of each community is critical, but difficult

50. Staying Relevant (2) • In addition to staying relevant to the experiments, GriPhyN must also be relevant to the greater scientific community • To CS researchers • To similarly data-intensive projects • Easy to understand code, concepts, APIs, etc. • How do you accommodate both a focused client community and the broader scientific community • Common challenge across many CI initiatives