  Computational inference of causal relationships underlying complex networks,
such as gene-regulatory pathways, is NP-complete due to its combinatorial
nature when permuting all possible interactions. Markov chain Monte Carlo
(MCMC) has been introduced to sample only part of the combinations while still
guaranteeing convergence and traversability, which therefore becomes widely
used. However, MCMC is not able to perform efficiently enough for networks that
have more than 15~20 nodes because of the computational complexity. In this
paper, we use general purpose processor (GPP) and general purpose graphics
processing unit (GPGPU) to implement and accelerate a novel Bayesian network
learning algorithm. With a hash-table-based memory-saving strategy and a novel
task assigning strategy, we achieve a 10-fold acceleration per iteration than
using a serial GPP. Specially, we use a greedy method to search for the best
graph from a given order. We incorporate a prior component in the current
scoring function, which further facilitates the searching. Overall, we are able
to apply this system to networks with more than 60 nodes, allowing inferences
and modeling of bigger and more complex networks than current methods.
