A Software Architecture for Application Driven High Performance Image Processing

The paper introduces a software architecture to support a user from the image processing community in the development of time-constrained image processing applications on parallel computers. The architecture is based on abstract data types with a well deened interface. The interface separates an application from the actual hardware used. On the application side of the interface the programmer is presented with a familiar (sequential) programming model. On the hardware side of the interface detailed knowledge of a parallel machine may be employed to arrive at eecient implementations of basic functionality. Knowledge of both suitable data distributions for images and performance characteristics of operations on those image allows for automated selection of an appropriate data distribution scheme throughout the application. Experiments show that with little eeort reasonable levels of eeciency and scalability are achieved on a 32-node MIMD architecture.

[1]  Susanne E. Hambrusch,et al.  The role of models, software tools, and applications in high performance computing , 1994 .

[2]  Martin C. Herbordt,et al.  Status and Current Research in the Image Understanding Architecture Effort. , 1991 .

[3]  Susanne E. Hambrusch,et al.  Parallel scalable libraries and algorithms for computer vision , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5).

[4]  H. Leah,et al.  Algorithm Scalability : A Poly-Algorithmic Approach , 1995 .

[5]  Philip J. Hatcher,et al.  Data-Parallel Programming on MIMD Computers , 1991, IEEE Trans. Parallel Distributed Syst..

[6]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Dominique Noguet,et al.  A Data Dependent Architecture Based on Seeded Region Growing Strategy for Advanced Morphological Operators , 1996, ISMM.

[8]  Joseph N. Wilson,et al.  Handbook of computer vision algorithms in image algebra , 1996 .