Embedded Immersive Engagement for Cyberinfrastructure on the Open Science Grid

1. Embedded Immersive Engagement for Cyberinfrastructure on the Open Science Grid John McGee – mcgee@renci.org Renaissance Computing Institute University of North Carolina, Chapel Hill

2. 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 iVDGL (NSF) OSG Grid3 GriPhyN Trillium (NSF) (DOE+NSF) PPDG (DOE) Context: Evolution of Projects

3. OSG Members OSG welcomes new members, partners and collaborators. OSG members today represent: - Universities - National laboratories and computing centers - Scientific collaborations - Grid projects and alliances OSG Consortium Meeting, August 2006

4. The OSG Vision Transform compute and data intensive science through a cross-domain self-managednational distributed cyberinfrastructure that Brings together campus and community Infrastructure and facilitating the needs of Virtual Organizations at all scales

5. The Open Science Grid A framework for large scale distributed resource sharing addressing the technology, policy, and social requirements of sharing OSG is a consortium of software, service and resource providers and researchers, from universities, national laboratories and computing centers across the U.S., who together build and operate the OSG project. The project is funded by the NSF and DOE, and provides staff for managing various aspects of the OSG. Brings petascale computing and storage resources into a uniform grid computing environment Integrates computing and storage resources from over 80 sites in the U.S. and beyond

6. Using OSG Today • Astrophysics • Biochemistry • Bioinformatics • Earthquake Engineering • Genetics • Gravitational-wave physics • Mathematics • Nanotechnology • Nuclear and particle physics • Text mining • And more…

7. Regional Grid Project HPC Resource Campus Grid Virtual Organizations (VOs) The OSG Infrastructure trades in Groups not Individuals Image courtesy: UNM Image courtesy: UNM Image courtesy: UNM

8. Collider Detector at Fermilab (CDF) Compact Muon Solenoid (CMS) CompBioGrid (CompBioGrid) D0 Experiment at Fermilab (DZero) Dark Energy Survey (DES) Distributed Organization for Scientific and Academic Research (DOSAR) Engagement (Engage) Fermi National Accelerator Center (Fermilab) Functional Magnetic Resonance Imaging (fMRI) Geant4 Software Toolkit (geant4) Genome Analysis and Database Update (GADU) Georgetown University Grid (GUGrid) Great Plains Network (GPN) Grid Exerciser (GEx) (GridEx) Grid Laboratory of Wisconsin (GLOW) Grid Research and Education Group at Iowa (GROW) Group Researching Advances in Software Engineering at Buffalo (NYSGrid) Interactions in Understanding the Universe Initiative (i2u2) International Virtual Data Grid Laboratory (iVDGL) Laser Interferometer Gravitational-Wave Observatory (LIGO) nanoHUB Network for Computational Nanotechnology (NCN) (nanoHUB) Northwest Indiana Computational Grid (NWICG) Open Science Grid (OSG) OSG Monitoring Information System (MIS) OSG Operations Group (Ops) Sloan Digital Sky Survey (SDSS) Solenoidal Tracker at RHIC (STAR) United States ATLAS Collaboration (USATLAS) VIRTUAL ORGANIZATIONS (2/1/07) • Non-physics • Partner Grids VO crosses OSG & TeraGrid

9. What OSG does/does-not provide The OSG Facility does not – “Own” any compute (processing, storage and communication) resources “Own” any middleware Fund any site or VO administration/operation personnel Make a “one size fits all”. The OSG Facility does – Help sites join the OSG facility and enable effective guaranteed and opportunistic usage of their resources (including data) by remote users Help VOs join the OSG facility and enable effective guaranteed and opportunistic harnessing of remote resources (including data) Define interfaces which people can use. Maintain and supports an integrated software stack that meets the needs of the stakeholders of the OSG consortium Reach out to non-HEP communities to help them use the OSG Train new users, administrators, and software developers

11. Why should my University/Gov’t Lab facilitate(or drive) resource sharing? • Because it is the right thing to do • Enables new modalities of collaboration • Enables new levels of scale • Democratizes large scalecomputing • Sharing locally leads to sharingglobally • Better overall resource utilization • Funding agencies At the heart of the cyberinfrastructure vision is the development of a cultural community that supports peer-to-peer collaboration and new modes of education based upon broad and open access to leadership computing; data and information resources; online instruments and observatories; and visualization and collaboration services. Arden Bement CI Vision for 21st Century introduction

12. Engagement Mission • Facilitate University Campus CI deployment, and interconnect it with the national organizations • Help new user communities from diverse scientific domains adapt their computational systems to leverage OSG

13. New User/Provider Issues • OSG is a large and complicated entity with many moving parts. That’s simply what it takes! Big goals, high performing world class distributed infrastructure • A federation of like minded individuals and teams • Can seem very daunting to an individual researcher, especially for those not from physics/compSci, yet working to accomplish computational science • OSG must appear to be well coordinated with a unified message, especially during early stage contact, prior to a plateau of buy-in/value recognition by new users/providers

14. Engagement Process Embedding experts into research and campus infrastructure teams to bring specific applications onto OSG Build trusting relationships with researchers, students, campus IT leadership Recruit potential engagements via formal venues and the social networking activities of OSG staff

15. Clemson Campus Condor Pool Machines in 27 different locations on Campus ~1,700 job slots >1.8M hours served in6 months Users from Industrial and Chemical engineering, and Economics Fast ramp up of usage Accessible to the OSG through a gateway

16. 6,400 CPUs available Campus Condor pool backfills idle nodes in PBS clusters - provided 5.5 million CPU-hoursin 2006, all from idle nodes in clusters Use on TeraGrid: 2.4 million hours in 2006 spent Building a database of hypothetical zeolite structures; 2007: 5.5 million hours allocated to TG http://www.cs.wisc.edu/condor/PCW2007/presentations/cheeseman_Purdue_Condor_Week_2007.ppt

17. Grid Laboratory of Wisconsin (GLOW) 2003 Initiative, Six Initial GLOW Sites • Computational Genomics, Chemistry • Amanda, Ice-cube, Physics/Space Science • High Energy Physics/CMS, Physics • Materials by Design, Chemical Engineering • Radiation Therapy, Medical Physics • Computer Science Diverse users with different deadlines and usage patterns

18. Grid Laboratory of Wisconsin (GLOW) • Users submit jobs to their own private or department scheduler as members of a group (e.g. “CMS” or “MedPhysics”) • Jobs are dynamically matched to available machines • Jobs run preferentially at the “home” site, but may run anywhere when machines are available • Computers at each site give highest priority to jobs from same group (via machine RANK) • Crosses multiple administrative domains • No common uid-space across campus • No cross-campus NFS for file access

19. Date range: 2007-04-29 00:00:00 GMT - 2007-05-07 23:59:59 GMT

20. Active Science Areas • Biology • Biochemistry • Genetics • Information and Library Science • Coastal Modeling • Economics • Video Processing • Earthquake Simulation • Earth Sciences • Energy Research • Physics • Molecular Dynamics • 12 domains represented in current active engagements, • including a couple of SCIDac projects

21. Kuhlman: Biology Designing proteins that fold into specific structures and bind target molecules Millions of simulations lead to the creation of a few proteins in the wet-lab Brought to OSG’s attention by local campus research computing group that was being overwhelmed Assistant Professor and a lab of 5 graduate students One protein can fold in many ways. This computationally designed protein switches between a zinc finger structure and a coiled-coil structure, depending on its environment. http://www.isgtw.org/?pid=1000507

22. Designing proteins in the Kuhlman Lab For each protein we design, we consume about 5,000 CPU hours across 10,000 jobs,” says Kuhlman.  “Adding in the structure and atom design process, we’ve consumed about 250,000 CPU hours in total so far.”

23. 16 member ensemble run for fine grained (4km) forecast using WRF Validating accuracy of components within the WRF model when scaled from 30km to 4km Publication acknowledgement:http://personal.uncc.edu/betherto/ams-slc-nwp.pdf Led to analysis of MPI services: Etherton: Earth Sciences http://osg-docdb.opensciencegrid.org/0006/000674/001/OSG_MPI.pdf http://www.isgtw.org/?pid=1000860

24. Luettich: Coastal Modeling “All,  Kevin, Mats, and I set up a test of running the coarse/fast ADCIRC system on 50,000 Monte Carlo simulated tracks that impact NC.  The tracks are from Peter's track generation methods.  Kevin and Mats set up the 50K runs to run on the NSF/DOE Open Science Grid.  There are 2 images attached.  The first shows the jobs running on OSG, and the big, sustained blip of jobs is the submission and delegation of the 50K runs onto available compute resources. From the graph, it looks like the 50K runs took about 7 hours.  The second figure is the max elevation (in meters) at the 489 coastal nodes for all of the 50K tracks.  I haven't looked in detail at the results, although they look reasonable.  The main point is that this was fairly easy to do, and this will allow us to explore sensitivities to track selections for the Flood Plain Mapping simulations.“ – Brian Blanton

25. Blake: Information and Library Science "Claim Jumping through Scientific Literature" a collaborative research project with Dr. Catherine Blake, Assistant Professor in the School of Information and Library Science at UNC-CH investigating approaches to multi-document summarization of scientific literature across disciplines. natural language parsing (NLP) of a large sample set (162,000) of biomedical research papers from the TREC (Text Retrieval Conference - NIST) Genomics Track document collection. “Using the OSG for this task has reduced NLP analysis time for the TREC collection from weeks to only a few days. The dramatic reduction in running time has allowed us to experiment and to fix problems iteratively in the text preprocessing and NLP that would not have been possible on a multi-week time scale.”

26. … so, what can we do together … Campus Researcher Student Lab IT Department IT Department IT Campus IT and Enterprise Systems Research Computing Computer Science Dept • … to advance scientific research and education?

27. What can we do together? • OSG is looking for a few partners to help deploy campus wide infrastructure that integrates with local enterprise infrastructure and the national CI • OSG's Engagement team is available to help scientists get their applications running on OSG • low impact starting point • Help your researchers gain significant compute cycles while exploring OSG as a framework for your own campus CI

28. Questions? OSG Engagement VO https://twiki.grid.iu.edu/twiki/bin/view/Engagement/WebHome engage-team@opensciencegrid.org

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