Algorithmic skeletons: structured management of parallel computation
Algorithmic skeletons: structured management of parallel computation
Functional programming on a dataflow architecture: applications in real-time image processing
Machine Vision and Applications
A scalable, real-time, image processing pipeline
Machine Vision and Applications - Special issue: new architectural solutions for computer vision systems
Parallel skeletons for structured composition
PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
A Framework for Exploiting Task and Data Parallelism on Distributed Memory Multicomputers
IEEE Transactions on Parallel and Distributed Systems
Optimal use of mixed task and data parallelism for pipelined computations
Journal of Parallel and Distributed Computing
Skeletons in N dimensions using shape primitives
Pattern Recognition Letters
Computer Vision
NETRA: A Hierarchical and Partitionable Architecture for Computer Vision Systems
IEEE Transactions on Parallel and Distributed Systems
CPR: Mixed Task and Data Parallel Scheduling for Distributed Systems
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
EASY PIPE: An ``EASY to use'' Parallel Image processing Environment based on algorithmic skelekons
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
The CC/IPP, an MIMD-SIMD architecture for image processing and pattern recognition
CAMP '97 Proceedings of the 1997 Computer Architectures for Machine Perception (CAMP '97)
CAMP '97 Proceedings of the 1997 Computer Architectures for Machine Perception (CAMP '97)
Compiler Optimizations for Parallel Sparse Programs with Array Intrinsics of Fortran 90
ICPP '99 Proceedings of the 1999 International Conference on Parallel Processing
Parallel processing for image and video processing: Issues and challenges
Parallel Computing
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Large datasets, such as pixels and voxels in 2D and 3D images can usually be reduced during their processing to smaller subsets with less datapoints. Such subsets can be the objects in the image, features - edges or corners - or more general, regions of interest. For instance, the transformation from a set of datapoints representing an image, to one or more subsets of datapoints representing objects in the image, is due to a segmentation algorithm and may involve both the selection of datapoints as well as a change in datastructure. The massive number of pixels in the original image, points to a data parallel approach, whereas the processing of the various objects in the image is more suitable for task parallelism. In this paper we introduce a framework for parallel image processing and we focus on an array of buckets that can be distributed over a number of processors and that contains pointers to the data from the dataset. The benefit of this approach is that the processor activity remains focussed on the datapoints that need processing and, moreover, that the load can be distributed over many processors, even in a heterogeneous computer architecture. Although the method is generally applicable in the processing of sets, in this paper we obtain our examples from the domain of image processing. As this method yields speedups that are data dependent, we derived a run-time evaluation that is able to determine if the use of distributed buckets is beneficial.