Exploring Parallelism in Learning Belief Networks
This work addresses a specific bottleneck in learning belief networks for large domains, offering incremental improvements in speed and efficiency through parallelization.
The paper tackles the increased computational complexity of learning probabilistic domain models with multi-link look ahead search by using parallelism, proposing an algorithm that decomposes tasks for parallel processing and demonstrates effectiveness in implementation.
It has been shown that a class of probabilistic domain models cannot be learned correctly by several existing algorithms which employ a single-link look ahead search. When a multi-link look ahead search is used, the computational complexity of the learning algorithm increases. We study how to use parallelism to tackle the increased complexity in learning such models and to speed up learning in large domains. An algorithm is proposed to decompose the learning task for parallel processing. A further task decomposition is used to balance load among processors and to increase the speed-up and efficiency. For learning from very large datasets, we present a regrouping of the available processors such that slow data access through file can be replaced by fast memory access. Our implementation in a parallel computer demonstrates the effectiveness of the algorithm.