Parallelizing Probabilistic Inference: Some Early Explorations
This work addresses computational efficiency for probabilistic inference in AI, but it is incremental as it explores early parallelism methods without broad impact.
The study investigated parallelism opportunities for belief net inference on hypercube machines using SPI algorithms, finding substantial speedup only through parallelizing individual conformal product operations and dependent on appropriate factoring, with negligible parallelism at the topological level.
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.