Global Optimization for Mapping Parallel Image Processing Tasks on Distributed Memory Machines

Many parallel algorithms and library routines for computer vision and image processing (CVIP) tasks on distributed-memory multiprocessors are available. The typical image distribution may use column, row, and block based mapping. Integrating a set of library routines for a CVIP application requires a global optimization to determine the data mapping of individual tasks by considering inter-task communication. The main difficulty in deriving the optimal image data distribution for each task is that CVIP task computation may involve loops, and the number of processors available and the size of the input image may vary at the run time. In this paper, a CVIP application is modeled using a task chain with imperfectly nested loops, specified by conventional visual languages such asKhorosandExplorer. A mapping algorithm is proposed that optimizes the average run-time performance for CVIP applications with nested loops by considering the data redistribution overheads and possible run-time parameter variations. A taxonomy of CVIP operations is provided and used for further reducing the complexity of the algorithm. Experimental results on both low-level image processing and high-level computer vision applications are presented to validate this approach.