Scalable Parallel Implementations of List Ranking on Fine-Grained Machines

We present analytical and experimental results for ne-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 & 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 signiicant speedup on the MasPar MP-1 and MP-2, compared to the standard list ranking algorithms. The modiied algorithms exhibit good scalability and are robust across a wide variety of image types. We also show that load balancing on ne grained machines performs well only for large problem to machine size ratios.