Lazy Parallelization: A Finite State Machine Based Optimization Approach for Data Parallel Image Processing Applications

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International Parallel and Distributed Processing Symposium (IPDPS'03)

Nice, France

April 22-April 26

ISBN: 0-7695-1926-1

	ASCII Text	x
	F.J. Seinstra, D. Koelma, "Lazy Parallelization: A Finite State Machine Based Optimization Approach for Data Parallel Image Processing Applications," Parallel and Distributed Processing Symposium, International, pp. 229b, International Parallel and Distributed Processing Symposium (IPDPS'03), 2003.

	BibTex	x
	@article{ 10.1109/IPDPS.2003.1213418, author = {F.J. Seinstra and D. Koelma}, title = {Lazy Parallelization: A Finite State Machine Based Optimization Approach for Data Parallel Image Processing Applications}, journal ={Parallel and Distributed Processing Symposium, International}, volume = {0}, year = {2003}, issn = {1530-2075}, pages = {229b}, doi = {http://doi.ieeecomputersociety.org/10.1109/IPDPS.2003.1213418}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Parallel and Distributed Processing Symposium, International TI - Lazy Parallelization: A Finite State Machine Based Optimization Approach for Data Parallel Image Processing Applications SN - 1530-2075 SP EP A1 - F.J. Seinstra, A1 - D. Koelma, PY - 2003 KW - null VL - 0 JA - Parallel and Distributed Processing Symposium, International ER -

F.J. Seinstra, University of Amsterdam

D. Koelma, University of Amsterdam

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IPDPS.2003.1213418

Performance obtained with existing library-based parallelization tools for implementing high performance image processing applications is often sub-optimal. This is because inter-operation optimization (or: optimization across library calls) is often not incorporated in the library implementations. This paper presents a simple, efficient, finite state machine-based method for global performance optimization, called ?lazy parallelization?. Experimental results based on this approach show significant performance improvements over non-optimized parallel implementations.

Citation:

F.J. Seinstra, D. Koelma, "Lazy Parallelization: A Finite State Machine Based Optimization Approach for Data Parallel Image Processing Applications," ipdps, pp.229b, International Parallel and Distributed Processing Symposium (IPDPS'03), 2003

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