21st Century e-Knowledge Requires a High Performance e-Infrastructure

584 views

Published on

11.12.09
Keynote Presentation
40-year anniversary Celebration of SARA
Title: 21st Century e-Knowledge Requires a High Performance e-Infrastructure
Amsterdam, Netherlands

Published in: Technology, Education
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
584
On SlideShare
0
From Embeds
0
Number of Embeds
81
Actions
Shares
0
Downloads
5
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide
  • The team is placing large eddy simulation (LES) methods into classic computational fluid dynamics (CFD) Reynolds-averaged Navier-Stokes (RANS) codes to see if they can more accurately model the physics than what is currently possible with the original turbulence models. The team is modifying two NASA simulation platforms currently used by the wind and rotorcraft industries, OVERFLOW and FUN3D, in order to better account for some of the dominant sources of aeroacoustically generated noise, such as turbulence-induced and blade-vortex-induced noise. The team is placing large eddy simulation (LES) methods into classic computational fluid dynamics (CFD) Reynolds-averaged Navier-Stokes (RANS) codes to see if they can more accurately model the physics than what is currently possible with the original turbulence models. The team is modifying two NASA simulation platforms currently used by the wind and rotorcraft industries, OVERFLOW and FUN3D, in order to better account for some of the dominant sources of aeroacoustically generated noise, such as turbulence-induced and blade-vortex-induced noise."Whereas the RANS models usually can pick up the gross features, if there is a lot of turbulence involved—a lot of flow separation—then they miss a lot of the secondary and tertiary features that can be important for performance, for vibration, and also for noise," Smith explains.The team is also investigating the efficiency and accuracy of combining LES and adaptive mesh refinement (AMR) methods in the turbine wakes. Blades rotating against the stationary tower and nacelle—the housing for the gears and dynamo—generate a very dynamic and turbulent wake dominated by powerful tip vortices and separated flow closer to the center. This dynamic nature often means there are regions of the wake with either too much or too little grid resolution just for an instant, increasing the computation time or degrading the results. Linking LES with adaptive meshing, refining, and coarsening focuses computational power where and when it is needed. This task won't be easy; adaptive meshing presents many complications, such as feature detection and load-balancing. But, once these difficulties are overcome, the outcome will be a better result at a lower price, according to Christopher Stone, a local small‑business subcontractor of Computational Science and Engineering (LLC) and member of Smith's Georgia Tech team. The team is running their simulations on NCSA's Mercury cluster, an Intel Itanium 2 cluster with1,774 nodes, 10TF. "Whereas the RANS models usually can pick up the gross features, if there is a lot of turbulence involved—a lot of flow separation—then they miss a lot of the secondary and tertiary features that can be important for performance, for vibration, and also for noise," Smith explains.The team is also investigating the efficiency and accuracy of combining LES and adaptive mesh refinement (AMR) methods in the turbine wakes. Blades rotating against the stationary tower and nacelle—the housing for the gears and dynamo—generate a very dynamic and turbulent wake dominated by powerful tip vortices and separated flow closer to the center. This dynamic nature often means there are regions of the wake with either too much or too little grid resolution just for an instant, increasing the computation time or degrading the results. Linking LES with adaptive meshing, refining, and coarsening focuses computational power where and when it is needed. This task won't be easy; adaptive meshing presents many complications, such as feature detection and load-balancing. But, once these difficulties are overcome, the outcome will be a better result at a lower price, according to Christopher Stone, a local small‑business subcontractor of Computational Science and Engineering (LLC) and member of Smith's Georgia Tech team.
  • 21st Century e-Knowledge Requires a High Performance e-Infrastructure

    1. 1. ―21st Century e-Knowledge Requires a High Performance e-Infrastructure‖ Keynote Presentation 40-year anniversary Celebration of SARA Amsterdam, Netherlands December 9, 2011 Dr. Larry SmarrDirector, California Institute for Telecommunications and Information Technology Harry E. Gruber Professor, Dept. of Computer Science and Engineering Jacobs School of Engineering, UCSD 1 http://lsmarr.calit2.net
    2. 2. AbstractOver the next decade, advances in high performance computing will usher in anera of ultra-realistic scientific and engineering simulation-- in fields as varied asclimate sciences, ocean observatories, radioastronomy, cosmology, biology, and medicine. Simultaneously, distributedscientific instruments, high-resolution video streaming, and the globalcomputational and storage cloud all generate terabytes to petabytes of data.Over the last decade, the U.S. National Science Foundation funded theOptIPuter project to research how user-controlled 10Gbps dedicated lightpaths(or ―lambdas‖) could provide direct access to global data repositories, scientificinstruments, and computational resources from ―OptIPortals,‖ PC clusterswhich provide scalable visualization, computing, and storage in the userscampus laboratory. All of these components can be integrated into a seamlesshigh performance e-infrastructure required to support a next generation e-knowledge data-driven society. In the Netherlands SARA and its partnerSURFnet has taken a global leadership role in building out and supporting sucha future-oriented e-infrastructure, enabling powerful computing, dataprocessing, networking, and visualization e-science services, necessary for thepursuit of solutions to an increasingly difficult set of scientific and societalchallenges
    3. 3. Leading Edge Applications of Petascale ComputersToday Are Critical for Basic Research and Practical Apps Flames Supernova Fusion Parkinson’s
    4. 4. Supercomputing the Future of Cellulosic Ethanol Renewable Fuels Atomic-Detail Model of the Lignocellulose of Softwoods.The model was built by Loukas Petridis of the ORNL CMBMolecular Dynamics of Cellulose (Blue) and Lignin (Green) Computing the Lignin Force Field & Combining With the Known Cellulose Force Field Enables Full Simulations of Lignocellulosic Biomass www.scidacreview.org/0905/pdf/biofuel.pdf
    5. 5. Supercomputersare Designing Quieter Wind Turbines Simulation of an Infinite-Span ―Flatback" Wind Turbine Airfoil Designed by the Netherlands Delft University of Technology Using NASAs FUN3D CFD Code Modified by Georgia Tech to Include a Hybrid RANS/LES Turbulence model Georgia Institute of Technology Professor Marilyn Smith www.ncsa.illinois.edu/News/Stories/Windturbines/
    6. 6. Increasing the Efficiency of Tractor Trailers Using SupercomputersOak Ridge Leadership Computing Facility & the Viz Team (Dave Pugmire, Mike Matheson, and Jamison Daniel) BMI Corporation, an engineering services firm has teamed up with ORNL, NASA, and several BMI corporate partners with large trucking fleets
    7. 7. Realistic Southern CaliforniaEarthquake Supercomputer Simulations Magnitude 7.7 Earthquake http://visservices.sdsc.edu/projects/scec/terashake/2.1/
    8. 8. Tornadogenesis From Severe Thunderstorms Simulated by Supercomputer Source: Donna Cox, Robert Patterson, Bob Wilhelmson, NCSA
    9. 9. Improving Simulation of the Distribution of Water Vapor in the Climate System ORNL Simulations by Jim Hack; Visualizations by Jamison Daniel http://users.nccs.gov/~d65/CCSM3/TMQ/TMQ_CCSM3.html
    10. 10. 21st Century e-Knowledge Cyberinfrastructure: Built on a 10Gbps ―End-to-End‖ Lightpath Cloud HD/4k Live Video HPC Local or Remote Instruments End User OptIPortal 10G LightpathsCampusOptical Switch Data Repositories & Clusters HD/4k Video Repositories
    11. 11. The Global Lambda Integrated Facility-- Creating a Planetary-Scale High Bandwidth CollaboratoryResearch Innovation Labs Linked by 10G Dedicated Lambdas www.glif.is/publications/maps/GLIF_5-11_World_2k.jpg
    12. 12. SURFnet – a SuperNetwork Connecting to the Global Lambda Integrated Facility www.glif.is Visualization courtesy of Donna Cox, Bob Patterson, NCSA.
    13. 13. The OptIPuter Project: Creating High Resolution PortalsOver Dedicated Optical Channels to Global Science Data Scalable OptIPortal Adaptive Graphics Environment (SAGE) Picture Source: Mark Ellisman, David Lee, Jason Leigh Calit2 (UCSD, UCI), SDSC, and UIC Leads—Larry Smarr PI Univ. Partners: NCSA, USC, SDSU, NW, TA&M, UvA, SARA, KISTI, AIST Industry: IBM, Sun, Telcordia, Chiaro, Calient, Glimmerglass, Lucent
    14. 14. The Latest OptIPuter Innovation:Quickly Deployable Nearly Seamless OptIPortables 45 minute setup, 15 minute tear-down with two people (possible with one) Shipping Case Image From the Calit2 KAUST Lab
    15. 15. The OctIPortableCalit2/KAUST at SIGGRAPH 2011 Photo:Tom DeFanti
    16. 16. 3D Stereo Head Tracked OptIPortal: NexCAVE Array of JVC HDTV 3D LCD Screens KAUST NexCAVE = 22.5MPixels www.calit2.net/newsroom/article.php?id=1584 Source: Tom DeFanti, Calit2@UCSD
    17. 17. Green Initiative: Can Optical Fiber Replace Airline Travel for Continuing Collaborations ?Source: Maxine Brown, OptIPuter Project Manager
    18. 18. EVL’s SAGE OptIPortal VisualCasting Multi-Site OptIPuter Collaboratory CENIC CalREN-XD Workshop Sept. 15, 2008 Total Aggregate VisualCasting Bandwidth for Nov. 18, 2008EVL-UI Chicago Sustained 10,000-20,000 Mbps! At Supercomputing 2008 Austin, Texas November, 2008 Streaming 4k SC08 Bandwidth Challenge Entry Remote: On site: U of Michigan SARA (Amsterdam) UIC/EVLU Michigan GIST / KISTI (Korea) U of Queensland Osaka Univ. (Japan) Russian Academy of Science Masaryk Univ. (CZ) Requires 10 Gbps Lightpath to Each Site Source: Jason Leigh, Luc Renambot, EVL, UI Chicago
    19. 19. High Definition Video Connected OptIPortals:Virtual Working Spaces for Data Intensive Research 2010 NASA Supports Two Virtual Institutes LifeSize HD Calit2@UCSD 10Gbps Link toNASA Ames Lunar Science Institute, Mountain View, CA Source: Falko Kuester, Kai Doerr Calit2; Michael Sims, Larry Edwards, Estelle Dodson NASA
    20. 20. Genomic Sequencing is Driving Big Data November 30, 2011
    21. 21. BGI—The Beijing Genome Institute is the World’s Largest Genomic Institute• Main Facilities in Shenzhen and Hong Kong, China – Branch Facilities in Copenhagen, Boston, UC Davis• 137 Illumina HiSeq 2000 Next Generation Sequencing Systems – Each Illumina Next Gen Sequencer Generates 25 Gigabases/Day• Supported by Supercomputing ~160TF, 33TB Memory – Large-Scale (12PB) Storage
    22. 22. Using Advanced Info Tech and Telecommunications to Accelerate Response to Wildfires Early on October 23, 2007, Harris Fire San Diego Photo by Bill Clayton, http://map.sdsu.edu/
    23. 23. NASA’s Aqua Satellite’s MODIS Instrument Pinpoints the 14 SoCal FiresCalit2, SDSU, and NASA Goddard Used NASA Prioritization and OptIPuter Links to Cut time to Receive Images from 24 to 3 Hours October 22, 2007 Moderate Resolution Imaging Spectroradiometer (MODIS) NASA/MODIS Rapid Response www.nasa.gov/vision/earth/lookingatearth/socal_wildfires_oct07.html
    24. 24. High Performance Sensornets WIDC PSAP KYVW COTD KNW B08 1 BDC GVDA PFO WMC Santa Rosa RDM AZRY CRY SND BZN KSW FRD SMER SO DHL P474 SLMS MPO LVA2 Hans-Werner P478 BVDA SCS GLRS Braun, HPWREN PI P486 MTGY MVFD P510 P483 CRRS WLA GMPK DSME RMNA CWC USGC P506 P499 P480 P509 CE MONP UCSD70+ miles DESC MLO P497to SCI P494 P473 IID2 SDSU P500 CNM 155Mbps FDX PL 6 GHz FCC licensed POTR P066 155Mbps FDX 11 GHz FCC licensed NSS to CI and 45Mbps FDX 6 GHz FCC licensed S PEMEX 45Mbps FDX 11 GHz FCC licensed approximately 50 miles: Backbone/relay node 45Mbps FDX 5.8 GHz unlicensed Astronomy science site 45Mbps-class HDX 4.9GHz 45Mbps-class HDX 5.8GHz unlicensed Biology science site ~8Mbps HDX 2.4/5.8 GHz unlicensed HPWREN Topology, Earth science site ~3Mbps HDX 2.4 GHz unlicensed University site 115kbps HDX 900 MHz unlicensed August 2008 Researcher location 56kbps via RCS network Native American site dashed = planned First Responder site
    25. 25. Situational Awareness for Wildfires: Combining HD VTC with Satellite Images, HPWREN Cameras & Sensors Ron Robers, San Diego County Supervisor Howard Windsor, San Diego CalFIRE Chief Source: Falko Kuester, Calit2@UCSD
    26. 26. The NSF-Funded Ocean Observatory Initiative With aCyberinfrastructure for a Complex System of Systems Source: Matthew Arrott, Calit2 Program Manager for OOI CI
    27. 27. From Digital Cinema to Scientific Visualization: JPL Simulation of Monterey Bay 4k Resolution Source: Donna Cox, Robert Patterson, NCSA Funded by NSF LOOKING Grant
    28. 28. OOI CI is Built on NLR/I2 Optical Infrastructure Physical Network Implementation Source: JohnOrcutt, MatthewArrott, SIO/Calit2
    29. 29. A Near Future MetagenomicsFiber Optic Cable Observatory Source John Delaney, UWash
    30. 30. NSF Funds a Big Data Supercomputer: SDSC’s Gordon-Dedicated Dec. 5, 2011• Data-Intensive Supercomputer Based on SSD Flash Memory and Virtual Shared Memory SW – Emphasizes MEM and IOPS over FLOPS – Supernode has Virtual Shared Memory: – 2 TB RAM Aggregate – 8 TB SSD Aggregate – Total Machine = 32 Supernodes – 4 PB Disk Parallel File System >100 GB/s I/O• System Designed to Accelerate Access to Massive Data Bases being Generated in Many Fields of Science, Engineering, Medicine, and Social Science Source: Mike Norman, Allan Snavely SDSC
    31. 31. Rapid Evolution of 10GbE Port Prices Makes Campus-Scale 10Gbps CI Affordable • Port Pricing is Falling • Density is Rising – Dramatically • Cost of 10GbE Approaching Cluster HPC Interconnects$80K/portChiaro(60 Max) $ 5K Force 10 (40 max) ~$1000 (300+ Max) $ 500 Arista $ 400 48 ports Arista 48 ports2005 2007 2009 2010 Source: Philip Papadopoulos, SDSC/Calit2
    32. 32. Arista Enables SDSC’s Massive Parallel 10G Switched Data Analysis Resource10Gbps OptIPuter UCSD RCI Radical Change Enabled by Co-Lo Arista 7508 10G Switch 5 384 10G Capable 8 CENIC/ 2 32 NLR Triton 4 Existing 8 Commodity Trestles 32 2 Storage 100 TF 12 1/3 PB 40128 8 Dash 2000 TB Oasis Procurement (RFP) > 50 GB/s 128 • Phase0: > 8GB/s Sustained Today Gordon • Phase I: > 50 GB/sec for Lustre (May 2011) :Phase II: >100 GB/s (Feb 2012) Source: Philip Papadopoulos, SDSC/Calit2
    33. 33. The Next Step for Data-Intensive Science: Pioneering the HPC Cloud

    ×