Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Load Balancing Requirements in Parallel Implementations of Image Feature Extraction Tasks
IEEE Transactions on Parallel and Distributed Systems
Practical Parallel Algorithms for Dynamic Data Redistribution, Median Finding, and Selection
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
A fast asynchronous algorithm for linear feature extraction on IBM SP-2
CAMP '95 Proceedings of the Computer Architectures for Machine Perception
Load balancing strategies for symbolic vision computations
HIPC '96 Proceedings of the Third International Conference on High-Performance Computing (HiPC '96)
| Hi-index | 0.00 |
It is challenging to parallelize the problems with irregular computation and communication. In this paper, we proposed a scalable and efficient algorithm for solving irregular inter-processor data dependency in image understanding tasks on distributed memory machines. Depending on the input data, in the scatter phase, each processor distributes search requests to collect the remote data satisfying certain geometric constraints. In the gather phase, the requested remote data are collected by using a DataZone data structure and are returned to each processor. For demonstrating the usefulness of our algorithm, we conducted experiments on an IBM SP2. The experimental results were consistent with the theoretical analyses, and showed the scalability and efficiency of the proposed algorithm. Our code using C and MPI is portable onto other High Performance Computing(HPC) platforms.