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High-Performance Distributed Multimedia Computing

High-Performance Distributed Multimedia Computing . Frank Seinstra, Jan-Mark Geusebroek. MultimediaN (BSIK Project). Intelligent Systems Lab Amsterdam Informatics Institute University of Amsterdam. MultimediaN and DAS-3. MultimediaN and high-performance computing.

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High-Performance Distributed Multimedia Computing

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  1. High-Performance Distributed Multimedia Computing Frank Seinstra, Jan-Mark Geusebroek MultimediaN (BSIK Project) Intelligent Systems Lab AmsterdamInformatics InstituteUniversity of Amsterdam

  2. MultimediaN and DAS-3

  3. MultimediaN and high-performance computing Van Essen et al. Science 255, 1999.

  4. automatic analysis? A Real Problem, part 1… • News Broadcast - September 21, 2005 (see video1.wmv) • Police investigating over 80.000 (!) CCTV recordings • First match found no earlier than 2.5 months after July 7 attacks

  5. 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 … !!!

  6. Since 1998: “Parallel-Horus” Beowulf-type Clusters - familiar programming - easy execution High Performance Computing • Solution: • Very, very large scale parallel and distributed computing • New Problem: • Very, very complicated software Solution: tool to make parallel & distributed computing transparent to user User Wide-Area Grid Systems

  7. +/- 18 patterns (MPI) Parallel-Horus: Features (1) • Sequential programming: Parallel-Horus Sequential API Parallelizable Patterns Seinstra et al., Parallel Computing, 28(7-8):967-993, August 2002

  8. Don’t do this: Scatter ImageOp Gather Scatter ImageOp Gather Do this: Scatter ImageOp Avoid Communication ImageOp Gather On the fly! Parallel-Horus: Features (2) • Lazy Parallelization: Seinstra et al., IEEE Trans. Par. Dist. Syst., 15(10):865-877, October 2004

  9. 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? • Grid connectivity problems?

  10. Color-Based Object Recognition (1) • Our Solution: • Place ‘retina’ over input image • Each of 37 ‘retinal areas’ serves as a ‘receptive field’ • For each receptive field: • Obtain set of local histograms, invariant to shading / lighting • Estimate Weibull parameters ß and γ for each histogram • Hence: scene description by set of 37x4x3 = 444 parameters + = Geusebroek, British Machine Vision Conference, 2006.

  11. Color-Based Object Recognition (2) • Learning phase: • Set of 444 parameters is stored in database • So: learning from 1 example, under single visual setting “a hedgehog” • Recognition phase: • Validation by showing objects under at least 50 different conditions: • Lighting direction • Lighting color • Viewing position

  12. Amsterdam Library of Object Images (ALOI) • In laboratory setting: • 300 objects correctly recognized under all (!) visual conditions • 700 remaining objects ‘missed’ under extreme conditions only Geusebroek et al., Int. J. Comput. Vis.. 61(1):103-112, January 2005

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

  14. Example: Object Recognition (see video2.wmv) Demonstrated live (a.o.) at ECCV 2006, June 8-11, 2006, Graz, Austria

  15. 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

  16. 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

  17. 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 • But most of all: • DAS-3 very significant for future MM research

  18. The End (see video3.avi)

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