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parallelization strategy

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  • 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

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