In this work, we propose a speculative parallelization strategy for Probabilistic Search algorithms. We design a parallel version of one such algorithm for a real-time computer vision problem with many practical applications. The implementation is performed on a cluster of eight DEC AlphaServer 2100 4/233 machines connected via a DEC Memory Channel network. Four types of run-time systems are tested: Hardware-coherent Shared Memory (HSM), Distributed Shared Memory (DSM) with software coherence (Cashmere-2L), Reflective Shared Memory (RSM), and message passing (Digital PVM). A runtime system that is normally not the most efficient among these four (RSM) is found to be the best choice for our combination of alg~ rithm and architecture. This shows that algorithmic parallelization and the selection of an implementation environment mutually interfere with each other and must be performed in a synergistic manner.

Evaluating the Trade-Offs in the Parallelization of Probabilistic Search Algorithms

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Evaluating the Trade-Offs in the Parallelization of Probabilistic Search Algorithms