A simple parallel tree contraction algorithm
Journal of Algorithms
Faster optimal parallel prefix sums and list ranking
Information and Computation
An introduction to parallel algorithms
An introduction to parallel algorithms
List ranking and list scan on the Cray C-90
SPAA '94 Proceedings of the sixth annual ACM symposium on Parallel algorithms and architectures
Isoefficiency: Measuring the Scalability of Parallel Algorithms and Architectures
IEEE Parallel & Distributed Technology: Systems & Technology
Efficient Parallel Processing of Image Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Algorithms for List Ranking and for Solving Graph Problems on the Hypercube
IEEE Transactions on Parallel and Distributed Systems
Scalable data parallel algorithms and implementations for object recognition
Scalable data parallel algorithms and implementations for object recognition
Portable list ranking: an experimental study
Journal of Experimental Algorithmics (JEA)
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We present analytical and experimental results for fine-grained list ranking algorithms. We compare the scalability of two representative algorithms on random lists, then address the question of how the locality properties of image edge lists can be used to improve the performance of this highly data-dependent operation. Starting with Wyllie's algorithm and Anderson and Miller's randomized algorithm as bases, we use the spatial locality of edge links to derive scalable algorithms designed to exploit the characteristics of image edges. Tested on actual and synthetic edge data, this approach achieves significant speedup on the MasPar MP-1 and MP-2, compared to the standard list ranking algorithms. The modified algorithms exhibit good scalability and are robust across a wide variety of image types. We also show that load balancing on fine grained machines performs well only for large problem to machine size ratios.