This document discusses developing a common parallelization strategy for topological operators on multi-core architectures. It proposes a Strategy for Divide and conquer (SDM) to parallelize operators like thinning. The document evaluates two implementations of parallel thinning - using a lock-based queue and a concurrent queue. It concludes the strategy is generally applicable and discusses extending it to other operators like crest restoring and watersheds, and introducing new parallel programming models.

1. 1 Is it an open door to common parallelization strategy for topological operators on multi-core multi-thread architecture ? R. MAHMOUDI – A3SI Laboratory– 2009 April

2. 2 Summary General framework Parallel thinning operator Future work Discussion R. MAHMOUDI – A3SI Laboratory– 2009 April

3. 3 Summary General framework Parallel thinning operator Future work Discussion R. MAHMOUDI – A3SI Laboratory– 2009 April

4. 4 General framework 1. Scientific and technical context (1) Image processingoperators Fourier Transformation Opening Thinning Dynamic redistribution Linear filters Closing Crest restoring Not-linear filters Euclidean Distance Transformation Thresholding Smoothing Attributed Filter Watershed Associated class Topological operators Morphological operators Local operators Point-to-Point operators Global operators R. MAHMOUDI – A3SI Laboratory– 2009 April

5. 5 General framework 1. Scientific and technical context (2) (Associated class) Vs (Parallelizationstrategies) Global operators Topological operators Morphological operators Local operators Point-to-Point operators Sienstra [1] (2002) Wilkinson [2] (2007) Meijster [3] [1] F. J. Seinstra, D. Koelma, and J. M. Geusebroek, “A software architecture for user transparent parallel image processing”. [2] M.H.F. Wilkinson, H. Gao, W.H. Hesselink, “Concurrent Computation of Attribute Filters on Shared Memory Parallel Machines”. [3] A. Meijster, J. B. T. M. Roerdink, and W. H. Hesselink, “A general algorithm for computing distance transforms in linear time” . R. MAHMOUDI – A3SI Laboratory– 2009 April

6. 6 General framework 2. Ph. D. objectives (1) Topological operators Thinning operator [1] common parallelization strategy Crest restoring [1] 2D and 3D smoothing [2] Watershed based on w-thinning [3] Watershed based on graph [4] Homotopic kernel transformation [5] Leveling kernel transformation [5] [1] M. Couprie, F. N. Bezerra, and G. Bertrand, “Topological operators for grayscale image processing”, [2] M. Couprie, and G. Bertrand, “Topology preserving alternating sequential filter for smoothing 2D and 3D objects”. [3] G. Bertrand, “On Topological Watersheds”. [4] J. Cousty, M. Couprie, L. Najman and G. Betrand “Weighted fusion graphs: Merging properties and watersheds”. [5] G. Bertrand, J. C. Everat, and M. Couprie, "Image segmentation through operators based on topology“ R. MAHMOUDI – A3SI Laboratory– 2009 April

7. 7 General framework 2. Ph. D. objectives (2) Main Architectural Classes SISD machines SIMD machines MISD machines MIMD Machine : (Execute several instruction streams in parallel on different data) Shared Memory Machine Distributed Memory System CPU1 CPU2 CPU3 CPUn Random Access Memory R. MAHMOUDI – A3SI Laboratory– 2009 April

8. 8 General framework 2. Ph. D. objectives (3) Needs Common parallelization strategy of topological operators on multi-core multithread architecture (MIMD Machines – Shared Memory System)? Main Objectives Unifyingparallelizationmethod of topologicaloperators class (Algorithmiclevel) Implementation Methodology and optimization techniques on multi-core multithread architecture (Architecture level). R. MAHMOUDI – A3SI Laboratory– 2009 April

9. 9 General framework Parallel thinning operator Future work Discussion R. MAHMOUDI – A3SI Laboratory– 2009 April

10. 10 Parallel thinning operator 1. Theoretical background Filtered thinning method that allows to selectively simplify the topology, based on a local contrast parameter λ. (b) filtered skeleton with λ = 10. (a) After Deriche gradient operator R. MAHMOUDI – A3SI Laboratory– 2009 April

11. 11 Parallel thinning operator 1. Parallelization strategy (1) Definesearch area Startparallelcharacterization Create new shared data structure End parallelcharacterization Mergemodifiedsearch area Restart process until stability R. MAHMOUDI – A3SI Laboratory– 2009 April

12. 12 Parallel thinning operator 1. Parallelization strategy (2) SDM-Strategy (Divide and conquer principle) Up level DATA PARALLELISM MIXED PARALLELISM Down level THREAD PARALLELISM R. MAHMOUDI – A3SI Laboratory– 2009 April

13. 13 Parallel thinning operator 1. Parallelization strategy (3) R. MAHMOUDI – A3SI Laboratory– 2009 April

14. 14 Parallel thinning operator 2. Coordination of threads (1) Thread 1 Thread 2 First implementation using a lock-based shared FIFO queue. Lock() Unlock() Push() Fail Success Blocked R. MAHMOUDI – A3SI Laboratory– 2009 April

15. 15 Parallel thinning operator 2. Coordination of threads (2) Thread 1 Thread 2 Lock() and access semaphore Unlock() and leave semaphore Semaphore Push() Second implementation using a private-shared concurrent FIFO queue R. MAHMOUDI – A3SI Laboratory– 2009 April

16. 16 Parallel thinning operator 3. Performance testing (1) R. MAHMOUDI – A3SI Laboratory– 2009 April

17. 17 Parallel thinning operator 3. Performance testing (2) First implementation using a lock-based shared FIFO queue. R. MAHMOUDI – A3SI Laboratory– 2009 April

18. 18 Parallel thinning operator 3. Performance testing (3) Second implementation using a private-shared concurrent FIFO queue R. MAHMOUDI – A3SI Laboratory– 2009 April

19. 19 Parallel thinning operator 4. Conclusion Non-specific nature of the proposed parallelization strategy. Threads coordination and communication during computing dependently parallel read/write for managing cache-resident data 1 2 R. MAHMOUDI – A3SI Laboratory– 2009 April

20. 20 General framework Parallel thinning operator Future work Discussion R. MAHMOUDI – A3SI Laboratory– 2009 April

21. 21 Future work 1. Extension SDM - Strategy Performance enhancement (speed up) Efficiency (work distribution) Cache miss ParallelThinning Operator IMBRICATE TWO Operators Crest restoring R. MAHMOUDI – A3SI Laboratory– 2009 April

22. 22 Future work 2. New parallel topological watershed % Achievement Parallelwatershed Operator SDM - Strategy Performance enhancement (speed up) Efficiency (work distribution) Cache miss 80% R. MAHMOUDI – A3SI Laboratory– 2009 April

23. 23 General framework Parallel thinning operator Future work Discussion R. MAHMOUDI – A3SI Laboratory– 2009 April

24. 24 Discussion Introduce future programming model (make it easy to write programs that execute efficiently on highly parallel C.S) Introduce new “Draft”to design and evaluate parallel programming models (instead of old benchmark) Maximize programmer productivity, future programming model must be more human-centric (than the conventional focus on hardware or application) R. MAHMOUDI – A3SI Laboratory– 2009 April

25. 25 R. MAHMOUDI – A3SI Laboratory– 2009 April