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21st Century e-Knowledge Requires a High Performance e-Infrastructure
The keynote presentation by Dr. Larry Smarr discusses the role of high-performance computing and e-infrastructure in advancing scientific simulation across various fields, including biology and climate science. The Optiputer project illustrates how dedicated lightpaths can facilitate access to global data and instruments, supporting the next generation of data-driven research. In collaboration with organizations like SURFnet, efforts are underway to create high bandwidth collaboratories and enhance capabilities in computational research and visualization services.
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21st Century e-Knowledge Requires a High Performance e-Infrastructure
- 1. ―21st Century e-KnowledgeRequires a High Performance e-Infrastructure‖ Keynote Presentation 40-year anniversary Celebration of SARA Amsterdam, Netherlands December 9, 2011 Dr. Larry Smarr Director, 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. Abstract Over the nextdecade, advances in high performance computing will usher in an era of ultra-realistic scientific and engineering simulation-- in fields as varied as climate sciences, ocean observatories, radio astronomy, cosmology, biology, and medicine. Simultaneously, distributed scientific instruments, high-resolution video streaming, and the global computational and storage cloud all generate terabytes to petabytes of data. Over the last decade, the U.S. National Science Foundation funded the OptIPuter project to research how user-controlled 10Gbps dedicated lightpaths (or ―lambdas‖) could provide direct access to global data repositories, scientific instruments, and computational resources from ―OptIPortals,‖ PC clusters which provide scalable visualization, computing, and storage in the user's campus laboratory. All of these components can be integrated into a seamless high performance e-infrastructure required to support a next generation e- knowledge data-driven society. In the Netherlands SARA and its partner SURFnet has taken a global leadership role in building out and supporting such a future-oriented e-infrastructure, enabling powerful computing, data processing, networking, and visualization e-science services, necessary for the pursuit of solutions to an increasingly difficult set of scientific and societal challenges
- 3. Leading Edge Applicationsof Petascale Computers Today Are Critical for Basic Research and Practical Apps Flames Supernova Fusion Parkinson’s
- 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 CMB Molecular 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. Supercomputers are Designing QuieterWind Turbines Simulation of an Infinite-Span ―Flatback" Wind Turbine Airfoil Designed by the Netherlands Delft University of Technology Using NASA's 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. Increasing the Efficiencyof Tractor Trailers Using Supercomputers Oak 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. Realistic Southern California EarthquakeSupercomputer Simulations Magnitude 7.7 Earthquake http://visservices.sdsc.edu/projects/scec/terashake/2.1/
- 8. Tornadogenesis From SevereThunderstorms Simulated by Supercomputer Source: Donna Cox, Robert Patterson, Bob Wilhelmson, NCSA
- 9. Improving Simulation ofthe 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. 21st Century e-KnowledgeCyberinfrastructure: Built on a 10Gbps ―End-to-End‖ Lightpath Cloud HD/4k Live Video HPC Local or Remote Instruments End User OptIPortal 10G Lightpaths Campus Optical Switch Data Repositories & Clusters HD/4k Video Repositories
- 11. The Global LambdaIntegrated Facility-- Creating a Planetary-Scale High Bandwidth Collaboratory Research Innovation Labs Linked by 10G Dedicated Lambdas www.glif.is/publications/maps/GLIF_5-11_World_2k.jpg
- 12. SURFnet – aSuperNetwork Connecting to the Global Lambda Integrated Facility www.glif.is Visualization courtesy of Donna Cox, Bob Patterson, NCSA.
- 13. The OptIPuter Project:Creating High Resolution Portals Over 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. The Latest OptIPuterInnovation: 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. The OctIPortable Calit2/KAUST atSIGGRAPH 2011 Photo:Tom DeFanti
- 16. 3D Stereo HeadTracked 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. Green Initiative: Can Optical Fiber Replace Airline Travel for Continuing Collaborations ? Source: Maxine Brown, OptIPuter Project Manager
- 18. EVL’s SAGE OptIPortalVisualCasting Multi-Site OptIPuter Collaboratory CENIC CalREN-XD Workshop Sept. 15, 2008 Total Aggregate VisualCasting Bandwidth for Nov. 18, 2008 EVL-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/EVL U 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. High Definition VideoConnected OptIPortals: Virtual Working Spaces for Data Intensive Research 2010 NASA Supports Two Virtual Institutes LifeSize HD Calit2@UCSD 10Gbps Link to NASA Ames Lunar Science Institute, Mountain View, CA Source: Falko Kuester, Kai Doerr Calit2; Michael Sims, Larry Edwards, Estelle Dodson NASA
- 20. Genomic Sequencing isDriving Big Data November 30, 2011
- 21. BGI—The Beijing GenomeInstitute 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. Using Advanced InfoTech 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. NASA’s Aqua Satellite’sMODIS Instrument Pinpoints the 14 SoCal Fires Calit2, 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. 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 UCSD 70+ miles DESC MLO P497 to 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. Situational Awareness forWildfires: 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. The NSF-Funded OceanObservatory Initiative With a Cyberinfrastructure for a Complex System of Systems Source: Matthew Arrott, Calit2 Program Manager for OOI CI
- 27. From Digital Cinemato Scientific Visualization: JPL Simulation of Monterey Bay 4k Resolution Source: Donna Cox, Robert Patterson, NCSA Funded by NSF LOOKING Grant
- 28. OOI CI is Built on NLR/I2 Optical Infrastructure Physical Network Implementation Source: John Orcutt, Matthew Arrott, SIO/Calit2
- 29. A Near FutureMetagenomics Fiber Optic Cable Observatory Source John Delaney, UWash
- 30. NSF Funds aBig 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. Rapid Evolution of10GbE Port Prices Makes Campus-Scale 10Gbps CI Affordable • Port Pricing is Falling • Density is Rising – Dramatically • Cost of 10GbE Approaching Cluster HPC Interconnects $80K/port Chiaro (60 Max) $ 5K Force 10 (40 max) ~$1000 (300+ Max) $ 500 Arista $ 400 48 ports Arista 48 ports 2005 2007 2009 2010 Source: Philip Papadopoulos, SDSC/Calit2
- 32. Arista Enables SDSC’sMassive Parallel 10G Switched Data Analysis Resource 10Gbps 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 40128 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. The Next Stepfor Data-Intensive Science: Pioneering the HPC Cloud
Editor's Notes
- #6 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.