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Optimization of tele-immersion codes
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Source ACM International Conference Proceeding Series; Vol. 383 archive
Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units table of contents
Washington, D.C.
Pages 85-93  
Year of Publication: 2009
ISBN:978-1-60558-517-8
Authors
Albert Sidelnik  University of Illinois at Urbana-Champaign
I-Jui Sung  University of Illinois at Urbana-Champaign
Wanmin Wu  University of Illinois at Urbana-Champaign
María Jesús Garzarán  University of Illinois at Urbana-Champaign
Wen-mei Hwu  University of Illinois at Urbana-Champaign
Klara Nahrstedt  University of Illinois at Urbana-Champaign
David Padua  University of Illinois at Urbana-Champaign
Sanjay J. Patel  University of Illinois at Urbana-Champaign
Publisher
ACM  New York, NY, USA
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ABSTRACT

As computational power increases, tele-immersive applications are an emerging trend. These applications make extensive demands on computational resources through their heavy use of real-time 3D reconstruction algorithms. Since computer vision developers do not necessarily have parallel programming expertise, it is important to give them the tools and capabilities to naturally express computer vision algorithms, yet retain high efficiency by exploiting modern GPU and large-scale multi-core platforms.

In this paper, we describe our optimization efforts for a tele-immersion application by tuning it for GPU and multi-core platforms. Additionally, we introduce a method to obtain portability, high performance, and increase programmer productivity.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Parallel Computing Research at Illinois: The Upcrc Agenda. Technical report, Dept. of Computer Science, Dept. of Electrical and Computer Engineering, Corrdinated Science Laboratory, Nov 2008.
 
2
G. Almasi, L. D. Rose, J. Moreira, and D. Padua. Programming for locality and parallelism with hierarchically tiled arrays. In In Proc. of the 16th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2003, pages 162--176. Springer-Verlag, 2003.
3
 
4
B. Delaunay. Sur la sphère vide. Izvestia Akademia Nauk SSSR, VII Seria, Otdelenie Matematicheskii i Estestvennyka Nauk, 7:793--800, 1934.
5
 
6
M. Harris. Optimizing Parallel Reduction in Cuda, 2007.
 
7
S.-H. Jung and R. Bajcsy. A Framework for Constructing Real-time Immersive Environments for Training Physical Activities. Journal of Multimedia, 1(7):9--17, 2006.
8
 
9
 
10
K. Nahrstedt, 2008. private communication.
 
11
NVIDIA. Nvidia Cuda Programming Guide 2.0, 2008.
 
12
J. H. Wolf. Programming methods for the Pentium III processor's streaming SIMD extensions using the VTune performance enhancement environment, May 1999.
 
13
 
14
15

Collaborative Colleagues:
Albert Sidelnik: colleagues
I-Jui Sung: colleagues
Wanmin Wu: colleagues
María Jesús Garzarán: colleagues
Wen-mei Hwu: colleagues
Klara Nahrstedt: colleagues
David Padua: colleagues
Sanjay J. Patel: colleagues