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Big Data Workshop Summary

Big Data Workshop Summary. Geoffrey Fox gcf@indiana.edu http://www.infomall.org http://www.futuregrid.org Director, Digital Science Center, Pervasive Technology Institute Associate Dean for Research and Graduate Studies,  School of Informatics and Computing Indiana University Bloomington.

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Big Data Workshop Summary

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  1. Big Data WorkshopSummary Geoffrey Fox gcf@indiana.edu http://www.infomall.orghttp://www.futuregrid.org Director, Digital Science Center, Pervasive Technology Institute Associate Dean for Research and Graduate Studies,  School of Informatics and Computing Indiana University Bloomington Virtual School for Computational Science and EngineeringJuly 30 2010

  2. Many Thanks! • Speakers • Collaboration (A-V) Specialists • FutureGrid support staff • Lunch and other support staff! • Tom Scavo and Virtual School leadership (Thom Dunning and Sharon Glotzer) • And Students!

  3. Confusion in a Nutshell! • Time of Change so • It’s Exciting • But its hard to tell a coherent story • In particular TeraGrid and Cloud approaches are not aligned • Partly as TeraGrid mainly simulation and partly as technologies are changing

  4. Applications • Astronomy, Computational Fluid Dynamics, Sensors – Szalay, Budavari • Life Sciences: (Biology/Bioinformatics/Cheminformatics) Qiu Poulain Wild Sun Grossman Figueiredo • Particle Physics: Qiu Poulain

  5. Data: Files Transport and I/O • Latham: I/O Architecture • Application – System Mapping • MPI IO • netCDF files for structured data (arrays) • HDF5 for datasets • Other application specific packages • Darshan performance monitoring • Examples: Flash and Volume Rendering • Miller: Data Capacitor at IU and TeraGrid; application examples • Will be mounted on FutureGrid • Lustre in action on Data Capacitor • Use for big files • Metadata • Tatineni: Lustre File System in detail • Architecture • Parallel I/O performance with many cores; choice of stripe size • Wide area File systems: GPFS DC Lustre(future) • Data Transfer: sshbbcpbbftpGridFTP • Data Management and archivin

  6. Cloud Technologies • Focus as many data processing problems are suitable for either clouds (IaaS) or Cloud technologies (MapReduce, Data Parallel File Systems, Tables) • Hadoop, Twister, Dryad, Sector(Sphere) • NOSQL (Grossman) versus SQL (Szalay) • Amazon EC2, Azure • FutureGrid supports Clouds, Grids, Parallel computing • Open Science Data Cloud supports data intensive clouds • Appliances make deployment easier and especially attractive for classes; Virtual Clusters and Networks; on VM’s or bare; GroupVPN supports VO sharing resources • You can deploy anywhere (maybe except TeraGrid!)

  7. Some issues • Szalay defined the Big Science Data problem • Disk I/O performance issues – Amdahl numbers, Graywulf • 100 TB practical limit today in science but Google/Microsoft are order(s) of magnitude larger • Data-compute collocation -- Cyberbricks • Budavari noted role of GPUs, SQL, communities and standards • Grossman posed SQL v NOSQL ; ACID v BASE; and Size issues (SQL doesn’t scale across datacenter)

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