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/.Amd mnt/lotus/host/home/jaishakthi/presentation/rmeet1/rmeet 1 /.Amd mnt/lotus/host/home/jaishakthi/presentation/rmeet1/rmeet 1 Presentation Transcript

  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS S.M. Jaisakthi September 9, 2009
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Research Motivation Research Motivation Image processing is the technique used to manipulate the image in order to enhance, restore or interpret the image. Sequential. Computationally intensive. Hence image analysis algorithms need more response time and lack scalability.
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Research Motivation Research Motivation These issues can be solved by parallelizing the existing sequential algorithms by exploiting the massive computational power of the parallel computers.
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Research Motivation Research Objective Research Objective To design parallel algorithms for image processing operations such as filtration, histograms, edge detection, image segmentation etc., cost effectively in terms of reduced parallel overheads and applying these algorithms for effective parallelization of image analysis applications.
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Parallel Algorithm Design Parallel Algorithm Design According to Foster the design of parallel algorithm consist of 4 stages[13] : Partitioning Communication Agglomeration Mapping
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Parallel Algorithm Design Parallel Algorithm Design
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Parallel Algorithm Design Partitioning Partitioning Decompose given problem into primitive task. Domain Decompositon Divide data into pieces Determine how to associate computations with the data Functional Decomposition Divide computation into pieces Determine how to associate data with the computations Design Strategy Redundant computation and redundant data structure storage are minimized. Tasks are roughly same size. Number of tasks is an increasing function of problem size.
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Parallel Algorithm Design Communication Communication Determine communication structure between tasks. Local Communication Global Communication Design Strategy Communication operations are balanced among Tasks. Each Task communicate with only small group of neighbours. Tasks can perform communications concurrently. Tasks can perform computations concurrently.
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Parallel Algorithm Design Agglomeration Agglomeration Combining task into larger task. Goal Reduces the communication overheads. Maintains scalability. Reduces software engineering cost. Design Strategy Replicated computations take less time. Agglomerated tasks have similar computational and communications costs.
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Parallel Algorithm Design Mapping Mapping Process of assigning task to processors. Goal Maximize processor utilization Minimize Interprocessor Communication. Design Strategy One task per processor and multiple task per processor design have been considered
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Performance Analysis Understand the barriers to higher performance. Calculates how much improvement can be obtained by increasing number of processors. Performance can be analysised using Amdahl’s Law Gustafson-Barsis’s Law The Karp-Flatt Metric Isoefficiency Metric
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Cost Effectiveness of a Parallel Algorithm The cost of a parallel algorithm is the product of its run time Tp and the number of processors used p. A parallel algorithm is cost optimal when its cost matches the run time of the best known sequential algorithm Ts for the same problem. SequentialExecutionTime Speedup S = ParallelExecutionTime Speedup Efficiency ε = Numberofprocessorsused A cost optimal parallel algorithm has speed up p and efficiency 1.
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Amdahl’s Law Amdahl’s Law If F is the fraction of a calculation that is sequential, and (1-F) is the fraction that can be parallelised, then the maximum speedup that can be achieved by using P processors is 1 (1−F ) F+ p Shows how execution time decreases as number of processors increases. Provides maximum speedup required to solve fixed size problem with respect to number of processors. Limitations Ignores parallel overhead - overestimates speedup Assumes problem as fixed size, so underestimates speedup achievable
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Amdahl’s Effect Amdahl’s Effect As the problem size increases, the inherently sequential portion decreases As the problem size increases, computation dominates the communication As the problem size increases, the speedup increases
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Gustafson-Barsis’s Law Gustafson-Barsis’s Law Given a parallel program solving a problem of size n using p processors, let s denote the fraction of total execution time spent in serial code. The maximum speedup ψ achievable by this program is ψ ≤ p + (1 − p)s
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Gustafson-Barsis’s Law Gustafson-Barsis’s Law Begin with parallel execution time Estimate sequential execution time to solve same problem Problem size is an increasing function of p Predicts scaled speedup
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Karp-Flatt Metric The Karp-Flatt Metric Amdahls Law and Gustafson-Barsis Law ignore Communication overhead They can overestimate speedup or scaled speedup Karp and Flatt proposed another metric
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Experimentally Determined Serial Fraction Experimentally Determined Serial Fraction Given a parallel computation exhibiting speedup Ψ on p processors, where p ≥ 1, the experimentally determined serial fraction e is defined to be the Karp - Flatt Metric 1 1 ψ −p e= 1 1− p Takes into account parallel overhead Detects other sources of overhead or inefficiency ignored in speedup model Process startup time Process synchronization time Imbalanced workload Architectural overhead
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Isoefficiency Metric Isoefficiency Metric Scalability of a parallel system: measure of its ability to increase performance as number of processors increases A scalable system maintains efficiency as processors are added Isoefficiency: Measures scalability
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Isoefficiency Metric Isoefficiency Metric In order to maintain the same level of efficiency as the number of processors increases, n must be increased so that the following inequality is satistied : T (n, 1) ≥ CT0 (n, p) where ε(n,p) C = (1−ε(n,p)) T0 (n, p) = (p − 1)σ(n) + pk(n, p)
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Isoefficiency Metric References Amir Hosein Kamalizad, Chengzhi Pan, Nader Bagherzadeh: Fast Parallel FFT on a Reconfigurable Computation Platform. SBAC-PAD 2003: 254-259 Bruno Galile, Franck Mamalet, Marc Renaudin, Pierre-Yves Coulon: Parallel Asynchronous Watershed Algorithm-Architecture. IEEE Trans. Parallel Distrib. Syst. 18(1): 44-56 (2007) Chan, K.L.Tsui, W.M.Chan, H.Y.Wong, H.Y.Lai, H.C., Parallelising image processing algorithms, IEEE Region 10 Conference on Computer, Communication, Control and Power Engineering, 1993, Vol. 2, PP. 942-944. Cristina Nicolescu, Pieter Jonker: EASY PIPE: An “EASY to use” Parallel Image processing Environment based on algorithmic skeletons. IPDPS 2001:
  • PARALLELIZATION OF IMAGE PROCESSING ALGORITHMS FOR EFFECTIVE IMAGE ANALYSIS Performance Analysis Isoefficiency Metric THANK YOU