2013/03/13 by Bruce D’Ambrosio, Bruce D'Ambrosio, Tony Fountain +4
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.AI
paper · pdf · doi:10.48550/arxiv.1303.5399
Appears in Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (UAI1992)
arxiv created 2013/03/13 · openalex publication_date 2013/03/13 · arxiv updated 2013/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We report on an experimental investigation into opportunities for parallelism in beliefnet inference. Specifically, we report on a study performed of the available parallelism, on hypercube style machines, of a set of randomly generated belief nets, using factoring (SPI) style inference algorithms. Our results indicate that substantial speedup is available, but that it is available only through parallelization of individual conformal product operations, and depends critically on finding an appropriate factoring. We find negligible opportunity for parallelism at the topological, or clustering tree, level.