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A High-Performance Campus-Scale Cyberinfrastructure: The Technical, Political, and Economic

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Larry Smarr
Larry Smarr

The document discusses UCSD's plans to build a high-performance campus-scale cyberinfrastructure to support data-intensive research. It outlines investments in fiber networks and large-scale storage resources to connect researchers and instruments campus-wide. A key part of the plan is the Triton resource, a shared cluster and storage system hosted at SDSC to enable large-scale data analysis across campus.

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A High-Performance Campus-Scale Cyberinfrastructure: The Technical, Political, and Economic Presentation by Larry Smarr to the NSF Campus Bridging Workshop October 11, 2010 Anaheim, CA 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 Follow me on Twitter: lsmarr
Academic Research “OptIPlatform” Cyberinfrastructure: An End-to-End 10Gbps Lightpath Cloud National LambdaRail Campus Optical Switch Data Repositories & Clusters HPC HD/4k Video Images HD/4k Video Cams End User OptIPortal 10G Lightpaths HD/4k Telepresence Instruments
“ Blueprint for the Digital University”--Report of the UCSD Research Cyberinfrastructure Design Team ,[object Object],research.ucsd.edu/documents/rcidt/RCIDTReportFinal2009.pdf No Data Bottlenecks--Design for Gigabit/s Data Flows April 2009
Broad Campus Input to Build the Plan and Support for the Plan ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Invest in Campus Research CI? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Invest Now? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementing the Campus Research CI Plan ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
UCSD Campus Investment in Fiber and Networks Enables High Performance Campus Bridging CI DataOasis (Central) Storage OptIPortal Tile Display Wall Campus Lab Cluster Digital Data Collections Triton – Petadata Analysis Gordon – HPC System Cluster Condo Scientific Instruments N x 10Gbe CENIC, NLR, I2DCN Source: Philip Papadopoulos, SDSC, UCSD
UCSD Planned Optical Networked Biomedical Researchers and Instruments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Cellular & Molecular Medicine West National Center for Microscopy & Imaging Biomedical Research Center for Molecular Genetics Pharmaceutical Sciences Building Cellular & Molecular Medicine East CryoElectron Microscopy Facility Radiology Imaging Lab Bioengineering [email_address] San Diego Supercomputer Center
Moving to a Shared Enterprise Data Storage and Analysis Resource:Triton Resource @ SDSC Triton Resource ,[object Object],[object Object],[object Object],[object Object],[object Object],x28 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],x256 Campus Research Network UCSD Research Labs ,[object Object],[object Object],[object Object],[object Object],[object Object]
Rapid Evolution of 10GbE Port Prices Makes Campus-Scale 10Gbps CI Affordable 2005 2007 2009 2010 $80K/port Chiaro (60 Max) $ 5K Force 10 (40 max) $ 500 Arista 48 ports ~$1000 (300+ Max) $ 400 Arista 48 ports ,[object Object],[object Object],[object Object],Source: Philip Papadopoulos, SDSC, UCSD
10G Switched Data Analysis Resource: Data Oasis (RFP Underway) 2 32 OptIPuter 32 Colo RCN CalRen Existing Storage 1500 – 2000 TB > 40 GB/s 24 20 Triton 8 Dash 100 Gordon Oasis Procurement (RFP) ,[object Object],[object Object],[object Object],[object Object],40
High Performance Computing (HPC) vs. High Performance Data (HPD) Source: SDSC Attribute HPC HPD Key HW metric Peak FLOPS Peak IOPS Architectural features Many small-memory multicore nodes Fewer large-memory vSMP nodes Typical application Numerical simulation Database query Data mining Concurrency High concurrency Low concurrency or serial Data structures Data easily partitioned e.g. grid Data not easily partitioned e.g. graph Typical disk I/O patterns Large block sequential Small block random Typical usage mode Batch process Interactive
GRAND CHALLENGES IN DATA-INTENSIVE SCIENCES OCTOBER 26-28, 2010 SAN DIEGO SUPERCOMPUTER CENTER , UC SAN DIEGO ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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A High-Performance Campus-Scale Cyberinfrastructure: The Technical, Political, and Economic

  • 1. A High-Performance Campus-Scale Cyberinfrastructure: The Technical, Political, and Economic Presentation by Larry Smarr to the NSF Campus Bridging Workshop October 11, 2010 Anaheim, CA 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 Follow me on Twitter: lsmarr
  • 2. Academic Research “OptIPlatform” Cyberinfrastructure: An End-to-End 10Gbps Lightpath Cloud National LambdaRail Campus Optical Switch Data Repositories & Clusters HPC HD/4k Video Images HD/4k Video Cams End User OptIPortal 10G Lightpaths HD/4k Telepresence Instruments
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  • 8. UCSD Campus Investment in Fiber and Networks Enables High Performance Campus Bridging CI DataOasis (Central) Storage OptIPortal Tile Display Wall Campus Lab Cluster Digital Data Collections Triton – Petadata Analysis Gordon – HPC System Cluster Condo Scientific Instruments N x 10Gbe CENIC, NLR, I2DCN Source: Philip Papadopoulos, SDSC, UCSD
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  • 13. High Performance Computing (HPC) vs. High Performance Data (HPD) Source: SDSC Attribute HPC HPD Key HW metric Peak FLOPS Peak IOPS Architectural features Many small-memory multicore nodes Fewer large-memory vSMP nodes Typical application Numerical simulation Database query Data mining Concurrency High concurrency Low concurrency or serial Data structures Data easily partitioned e.g. grid Data not easily partitioned e.g. graph Typical disk I/O patterns Large block sequential Small block random Typical usage mode Batch process Interactive
  • 14.