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… and Application Research Plans

… and Application Research Plans. Frank J. Seinstra. MultimediaN (BSIK Project). Intelligent Systems Lab Amsterdam Informatics Institute University of Amsterdam (Prof. Arnold Smeulders). MultimediaN and DAS-3. automatic analysis?. A Real Problem, part 1….

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… and Application Research Plans

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  1. … and Application Research Plans Frank J. Seinstra MultimediaN (BSIK Project) Intelligent Systems Lab AmsterdamInformatics InstituteUniversity of Amsterdam(Prof. Arnold Smeulders)

  2. MultimediaN and DAS-3

  3. automatic analysis? A Real Problem, part 1… • News Broadcast - September 21, 2005 • Police investigating over 80.000 (!) CCTV recordings • First match found no earlier than 2.5 months after July 7 attacks

  4. Image/Video Content Analysis • Lots of research + benchmark evaluations: • PASCAL-VOC (10,000+ images), TRECVID (200+ hours of video) • A Problem of scale: • At least 30-50 hours of processing time per hour of video! • Beeld&Geluid => 20.000 hours of TV broadcasts per year • NASA => over 850 Gb of hyper-spectral image data per day • London Underground => over 120.000 years of processing … !!!

  5. Since 1998: “Parallel-Horus” DAS-type Clusters High Performance Computing • Solution: Very large scale parallel and distributed computing • New Problem: Very complicated software Solution: tool to make parallel & distributed computing transparent to user User Wide-Area Grid Systems Seinstra et al.: IEEE Trans. Par. Dist. Syst., 13(7), July 2002IEEE Trans. Par. Dist. Syst., 15(10), October 2004Parallel Computing, 28(7-8), August 2002Concur. Comput.: Pract. Exp., 16(6), May 2004

  6. Extensions for Distributed Computing • Wide-Area Multimedia Services: Parallel Horus Client Parallel Horus Server Parallel Horus Servers Parallel Horus Servers Parallel Horus Client • User transparency? • Abstractions & techniques? • Integration: parallel/distributed?

  7. A Real Problem, part 2… + LambdaRAM ?? may be time-critical…!

  8. Example: Object Recognition See also: http://www.science.uva.nl/~fjseins/aibo.html

  9. Example: Object Recognition Demonstrated live (a.o.) at ECCV 2006, June 8-11, 2006, Graz, Austria

  10. Performance / Speedup on DAS-2 Single cluster, client side speedup Four clusters, client side speedup • Recognition on single machine: +/- 30 seconds • Using multiple clusters: up to 10 frames per second • Insightful: even ‘distant’ clusters can be used effectively for close to ‘real-time’ recognition

  11. Ok, robot dog is a funny/crazy toy application, but: • Best performer in TRECVID 2004 & TRECVID 2005 Snoek et al., IEEE Trans. Pattern Anal. Mach. Intell. in press, 2006 Results: applicability • Beneficial: • Performance gains largely obtained ‘for free’ • With Parallel-Horus we can build similar complex ‘Grid’ applications in a matter of hours…

  12. Current & Future Work • Very Large-Scale Distributed Multimedia Computing: • Overcome practical annoyances: • Software portability, firewall circumvention, authentication, … • Optimization and efficiency: • Tolerant to dynamic Grid circumstances, … • Systematic integration of MM-domain-specific knowledge, … • Deal with non-trivial communication patterns: • Heavy intra- & inter-cluster communication, … • Reach the end users: • Programming models, execution scenarios, … • Collaboration with VU (Prof. Henri Bal) & GridLab • Ibis: www.cs.vu.nl/ibis/ • Grid Application Toolkit: www.gridlab.org

  13. But most of all: DAS-3 MATTERS !!!… not only to ‘C’ …… but also to ‘I’ in ‘ASCI’ … Conclusions • Effective integration of results from two largely distinct research fields • Ease of programming => quick solutions • With DAS-3 / StarPlane we can start to take on much more complicated problems

  14. The End

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