Parallelizing Probabilistic Inference: Some Early Explorations
  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.
