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Science on the TeraGrid

Science on the TeraGrid. Daniel S. Katz d.katz@ieee.org Director of Science, TeraGrid GIG Senior Computational Researcher, Computation Institute, University of Chicago & Argonne National Laboratory Affiliate Faculty, Center for Computation & Technology, LSU

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Science on the TeraGrid

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  1. Science on the TeraGrid Daniel S. Katz d.katz@ieee.org Director of Science, TeraGrid GIG Senior Computational Researcher, Computation Institute, University of Chicago & Argonne National Laboratory Affiliate Faculty, Center for Computation & Technology, LSU Adjunct Associate Professor, Electrical and Computer Engineering Department, LSU

  2. Outline • Introduction to TeraGrid • Current Science • Future Science

  3. What is the TeraGrid • World’s largest distributed cyberinfrastructure for open scientific research, supported by US NSF • Integrated high performance computers (>2 PF HPC & >27000 HTC CPUs), data resources (>3 PB disk, >60 PB tape, data collections), visualization, experimental facilities (VMs, GPUs, FPGAs), network at 11 Resource Provider sites • Allocated to US researchers and their collaborators through national peer-review process • DEEP: provide powerful computational resources to enable research that can’t otherwise be accomplished • WIDE: grow the community of computational science and make the resources easily accessible • OPEN: connect with new resources and institutions • Integration: Single: portal, sign-on, help desk, allocations process, advanced user support, EOT, campus champions

  4. Governance • 11 Resource Providers (RPs) funded under separate agreements with NSF • Different start and end dates • Different goals • Different agreements • Different funding models • 1 Coordinating Body – Grid Integration Group (GIG) • University of Chicago/Argonne National Laboratory • Subcontracts to all RPs and six other universities • 7-8 Area Directors • Working groups with members from many RPs • TeraGrid Forum with Chair

  5. TG New Large Resources • Ranger@TACC • First NSF ‘Track2’ HPC system • 504 TF • 15,744 Quad-Core AMD Opteron processors • 123 TB memory, 1.7 PB disk • Kraken@NICS (UT/ORNL) • Second NSF ‘Track2’ HPC system • 1 PF Cray XT5 system • 16,512 compute sockets, 99,072 cores • 129 TB memory, 3.3 PB disk Blue Waters@NCSA NSF Track 1 10 PF peak Coming in 2011

  6. Who Uses TeraGrid (2008)

  7. How TeraGrid Is Used 2006 data

  8. How One Uses TeraGrid POPS (for now) User Portal Science Gateways Command Line Viz Service Data Service RP 1 RP 2 TeraGrid Infrastructure (Accounting, Network, Authorization,…) Network, Accounting, … RP 3 Compute Service Slide courtesy of Dane Skow and Craig Stewart

  9. User Portal: portal.teragrid.org

  10. Access to resources • Terminal: ssh, gsissh • Portal: TeraGrid user portal, Gateways • Once logged in to portal, click on “Login” • Also, SSO from command-line

  11. Science Gateways • A natural extension of Internet & Web 2.0 • Idea resonates with Scientists • Researchers can imagine scientific capabilities provided through familiar interface • Mostly web portal or web or client-server program • Designed by communities; provide interfaces understood by those communities • Also provide access to greater capabilities (back end) • Without user understand details of capabilities • Scientists know they can undertake more complex analyses and that’s all they want to focus on • TeraGrid provides tools to help developer • Seamless access doesn’t come for free • Hinges on very capable developer Slide courtesy of Nancy Wilkins-Diehr

  12. TeraGrid -> XD Future • Current RP agreements end in March 2011 • Except track 2 centers (current and future) • TeraGrid XD (eXtreme Digital) starts in April 2011 • Era of potential interoperation with OSG and others • New types of science applications? • Current TG GIG continues through July 2011 • Allows four months of overlap in coordination • Probable overlap between GIG and XD members • Blue Waters (track 1) production in 2011

  13. Outline • Introduction to TeraGrid • Current Science • Future Science

  14. Hurricanes and tornadoes cause massive loss of life and damage to property TeraGrid supported spring 2007 NOAA and University of Oklahoma Hazardous Weather Testbed Major Goal: assess how well ensemble forecasting predicts thunderstorms, including the supercells that spawn tornadoes Nightly reservation at PSC, spawning jobs at NCSA as needed for details Input, output, and intermediate data transfers Delivers “better than real time” prediction Used 675,000 CPU hours for the season Used 312 TB on HPSS storage at PSC TG App: Predicting storms Slide courtesy of Dennis Gannon, ex-IU, and LEAD Collaboration

  15. TG App: SCEC-PSHA • Part of SCEC (Tom Jordan, USC) • Using the large scale simulation data, estimate probablistic seismic hazard (PSHA) curves for sites in southern California (probability that ground motion will exceed some threshold over a given time period) • Used by hospitals, power plants, schools, etc. as part of their risk assessment • For each location, need a Cybershake run followed by roughly 840,000 parallel short jobs (420,000 rupture forecasts, 420,000 extraction of peak ground motion) • Parallelize across locations, not individual workflows • Completed 40 locations to date, targeting 200 in 2009, and 2000 in 2010 Managing these requires effective grid workflow tools for job submission, data management and error recovery, using Pegasus (ISI) and DAGman (Wisconsin) 15 Information/image courtesy of Phil Maechling

  16. TG App: GridChem Slide courtesy of Joohyun Kim

  17. TG Apps: Genius and Materials Fully-atomistic simulations of clay-polymer nanocomposites Modeling blood flow before (during?) surgery Why cross-site / distributed runs? Rapid turnaround, conglomeration of idle processors to run a single large job Run big compute & big memory jobs not possible on a single machine LAMMPS on TeraGrid HemeLB on LONI Slide courtesy of Steven Manos and Peter Coveney

  18. Outline • Introduction to TeraGrid • Current Science • Future Science

  19. ENZO • ENZO simulated cosmological structure formation • Big current production simulation: • 4096x4096x4096 non-adaptive mesh, 16 fields per mesh point • 64 billion dark matter particles • About 4000 MPI processes, 1-8 OpenMP threads per process • Reads 5 TB input data • Writes 8 TB data files • Restart reads latest 8 TB file • All I/O uses HDF5, each MPI process reading/writing their own data • Over few months for simulation, >100 data files written, >20 read for restarts • 24 hour batch runs • 5-10 data files output per run • Needs ~100 TB free disk space at start of run • (adaptive case is different, but I/O is roughly similar) Slide courtesy of Robert Harkness

  20. ENZO Calculation Stages • Generate initial conditions for density, matter velocity field, dark matter particle position and velocity (using parallel HDF5) • Using NICS Kraken w/ 4K MPI processes, though TACC Ranger is reasonable alternative • ~5 TB initial data created in 10 0.5-TB files • Decompose initial conditions for # of MPI tasks in simulation (using sequential HDF5) • Decomposition of mesh into "tiles" needed for MPI tasks requires strided reads in large data cube • Very costly on NICS Kraken, but can be done more efficiently on TACC Ranger • If done on Ranger, then 2 TB (4 512-GB files) must be transmitted from NICS to TACC, and after running MPI decomposition task (with 4K MPI tasks), 8K files (2 TB) must be returned to NICS • Dark matter particle sort onto "tiles" is most efficient on NICS Kraken because it has a superior interconnect • Sort usually run in 8 slices using 4K MPI tasks • Evolve time (using sequential HDF5) • Dump data files during run • Archive data files (8 TB every couple of hours -> 1100 MB/sec but NICS HPSS only reaches 300 MB/sec) • Derive data products • Capture 5-6 fields from each data file (~256 GB each) • Send to ANL or SDSC for data analysis or viz • Archive output of data analysis of viz (back at NICS) Overall run produces >1 PB data, >100 TB to be archived, >100 TB free disk space Slide courtesy of Robert Harkness

  21. Science Portals 2.0 • Workspace customized by user for specific projects • Pluggable into iGoogle or other compliant (and open source) containers • Integrates with user workspace to provide a complete and dynamic view of the user’s science, alongside other aspects of their lives (e.g. weather, news) • Integrates with social networks (e.g. FriendConnect, MySpace) to support collaborative science • TG User Portal provides this same information, but this view is more dynamic and more reusable, and can be more flexibly integrated into the user’s workspace • Gadgets suitable for use on mobile devices • Technology Detail • Gadgets are HTML/JavaScript embedded in XML • Gadgets conform to specs that are supported by many containers (iGoogle, Shindig, Orkut, MySpace) Resource Load gadget shows current view of load on available resources File Transfer gadget for lightweight access to data stores, simple global file searching, and reliable transfers Job Status gadget shows current view of job queues by site, user, status Domain science gadgets complement general purpose gadgets to encompass the full range of scientists’ interests Slide courtesy of Wenjun Wu, Thomas Uram, Michael Papka

  22. OLSGW Gadgets • OLSGW Integrates bio-informatics applications • BLAST, InterProScan, CLUSTALW , MUSCLE, PSIPRED, ACCPRO, VSL2 • 454 Pyrosequencing service under development • Four OLSGW gadgets have been published in the iGoogle gadget directory. Search for “TeraGrid Life Science”. Slide courtesy of Wenjun Wu, Thomas Uram, Michael Papka

  23. PolarGrid • Goal: Work with Center for Remote Sensing of Ice Sheets • Requirements: • View CReSIS data sets, run filters, and view results through Web map interfaces; • See/Share user’s events in a Calendar; • Update results to a common repository with appropriate access controls; • Post the status of computational experiments. • Support collaboration and information exchange by interfacing to blogs and discussion areas • Login Screen • Interface to create new users and login using existing accounts. • Integrated with OpenID API for authentication. Solution: Web 2.0-enabled PolarGrid Portal Slide courtesy of Raminder Singh, Gerald Guo, Marlon Pierce

  24. PolarGrid Home Page with a set of gadgets like Google Calendar, Picasa, Facebook, Blog, Twitter Slide courtesy of Raminder Singh, Gerald Guo, Marlon Pierce

  25. Google Gadgets & Satellite Data • Purdue: disseminating remote sensing products • Flash- and HTML-based gadgets, programmed by undergraduate students • Bring traffic/usage to a broad set of satellite products at Purdue Terrestrial Observatory • Backend system including commercial satellite data receivers, a smaller cluster, TG data collection, etc. • User control includes zoom, pause/resume, frame step through, etc. MODIS satellite viewer GOES-12 satellite viewer Slide courtesy of Carol Song

  26. Future App: Real-time High Resolution Radar Data • Delivering 3D visualization of radar data via a Google gadget • LiveRadar3D • Super high res, real-time NEXRAD data • Continuously updated as new data comes • 3D rendering that includes multiple stations in the US • Significant processing (high throughout) and rendering supported by TG systems • To be released next spring Slide courtesy of Carol Song

  27. Understanding Distributed ApplicationsDevelopment Objectives • Interoperability: Ability to work across multiple distributed resources • Distributed Scale-Out: The ability to utilize multiple distributed resources concurrently • Extensibility: Support new patterns/abstractions, different programming systems, functionality & Infrastructure • Adaptivity: Response to fluctuations in dynamic resource and availability of dynamic data • Simplicity: Accommodate above distributed concerns at different levels easily… Challenge: How to develop DA effectively and efficiently with the above as first-class objectives? Developed by Jha, Katz, Parashar, Rana, Weissman

  28. Montage Workflow 3 3 2 2 2 3 1 1 1 Final Mosaic(Overlapping Tiles) mConcatFit mProject 1 mProject 2 mProject 3 mAdd 2 mAdd 1 mBackground 1 mBackground 2 mBackground 3 mDiff 1 2 mDiff 2 3 David Hockney Pearblossom Highway 1986 D23 D12 a1x + b1y + c1 = 0 a2x + b2y + c2 = 0 a3x + b3y + c3 = 0 mFitplane D12 mFitplane D23 mBgModel ax + by + c = 0 dx + ey + f = 0 ax + by + c = 0 dx + ey + f = 0

  29. Montage on the Grid UsingPegasus Example DAG for 10 input files Maps an abstract workflow to an executable form mProject Pegasus http://pegasus.isi.edu/ mDiff mFitPlane mConcatFit mBgModel Grid Information Systems mBackground Data Stage-in nodes Information about available resources, data location mAdd Montage compute nodes Condor DAGMan Data stage-out nodes Executes the workflow Grid Registration nodes MyProxy User’s grid credentials

  30. DAG-based Workflow Applications:Extensibility Approach Application Development Phase Generation & Exec. Planning Phase Execution Phase Done with Andre Merzky, Katerina Stamou, Shantenu Jha

  31. SAGA-based DAG ExecutionPreserving Performance Done with Andre Merzky, Katerina Stamou, Shantenu Jha

  32. App: EnKF for Oil Reservoir Sim. • Ensemble Kalman Filters – recursive filters that can be used to handle large, noisy data; the data are sent through the Kalman filter to obtain the true state of the data • Used here to model oil field, w/ data from sensors, to build a model that represents reality • Using EnKF, scientific problem solved using “multiple models” (ensemble members) • Physical models represented as ensembles that vary in size from large MPI-style jobs to long-running single processor tasks • Varying parameters sometimes also lead to varying systems of equations and entirely new scenarios, increasing both computational and memory requirements • Each model must converge before the next stage can begin, hence dynamically load-balancing to ensure that all models complete as close to each other as possible is a desired aim • In the general case the number of jobs required varies between stages. • Suppose you want to perform a history match on a 1 million grid cell problem, with a thousand ensemble members • The entire system will have a few billion degrees of freedom • This will increase the need for autonomy, fault tolerance, self healing etc... 32 Credit: Yaakoub El-Khamra, Shantenu Jha

  33. EnKF Results: Scaling-Out • Using more machines decreases the TTC and variation between experiments • Using BQP (batch queue prediction) decreases the TTC & variation between experiments further • Lowest time to completion achieved when using BQP and all available resources R=Ranger, Q=Queen Bee, A=Abe Khamra & Jha, GMAC, ICAC’09 Credit: Yaakoub El-Khamra, Shantenu Jha

  34. TeraGrid: Both Operations and Research • Operations • Facilities/services on which researchers rely • Infrastructure on which other providers build AND • R&D • Learning how to do distributed, collaborative science on a global, federated infrastructure • Learning how to run multi-institution shared infrastructure http://teragrid.org/

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