A Data and Task Parallel Image Processing Environment

The paper presents a data and task parallel environment for parallelizing low-level image processing applications on distributed memory systems. Image processing operators are parallelized by data decomposition using algorithmic skeletons. At the application level we use task decomposition, based on the Image Application Task Graph. In this way, an image processing application can be parallelized both by data and task decomposition, and thus beter speed-ups can be obtained. The framework is implemented using C and MPI-Panda library and it can be easily ported to other distributed memory systems.

[1]  Thomas Rauber,et al.  Compiler support for task scheduling in hierarchical execution models , 1999, J. Syst. Archit..

[2]  Arjan J. C. van Gemund,et al.  CPR: mixed task and data parallel scheduling for distributed systems , 2001, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001.

[3]  Takeo Kanade,et al.  A Multiple-Baseline Stereo , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Sachin S. Sapatnekar,et al.  A Framework for Exploiting Task and Data Parallelism on Distributed Memory Multicomputers , 1997, IEEE Trans. Parallel Distributed Syst..

[5]  Jon A. Webb.  Implementation and performance of fast parallel multi-baseline stereo vision , 1993, 1993 Computer Architectures for Machine Perception.

[6]  Henri E. Bal,et al.  Experience with a Portability Layer for Implementing Parallel Prgroamming Systems , 1996, PDPTA.

[7]  Michael A. Saunders,et al.  USER’S GUIDE FOR SNOPT 5.3: A FORTRAN PACKAGE FOR LARGE-SCALE NONLINEAR PROGRAMMING , 2002 .

[8]  Peter A. Dinda,et al.  The CMU task parallel program suite , 1994 .

[9]  Jaspal Subhlok,et al.  A new model for integrated nested task and data parallel programming , 1997, PPOPP '97.

[10]  Ronald H. Perrott,et al.  Parallel programming , 1988, International computer science series.

[11]  Ioannis Pitas,et al.  Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks , 1993 .

[12]  Murray Cole,et al.  Algorithmic Skeletons: Structured Management of Parallel Computation , 1989 .

[13]  Jaspal Subhlok,et al.  Optimal Use of Mixed Task and Data Parallelism for Pipelined Computations , 2000, J. Parallel Distributed Comput..

[14]  Henri E. Bal,et al.  A task- and data-parallel programming language based on shared objects , 1998, TOPL.

[15]  Ronald L. Graham,et al.  Bounds on Multiprocessing Timing Anomalies , 1969, SIAM Journal of Applied Mathematics.

[16]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[17]  K. Mani Chandy,et al.  Fortran M: A Language for Modular Parallel Programming , 1995, J. Parallel Distributed Comput..