Using parallel techniques to improve the computational efficiency of evidential combination

This paper presents a method of partitioning a Markov tree of belief functions into clusters so as to efficiently implement parallel belief function propagations on the basis of the local computation technique. Our method initially represents computations of combining evidence on all nodes in a Markov tree as parallelism instances, then balances the computation load among these instances, and finally partitions them into clusters which can be mapped onto a set of processors in a PowerPC network. The advantage of our method is that the maximum parallelization can still be achieved, even with limited processor availability.