A Novel Learning Algorithm for Bayesian Network and Its Efficient Implementation on GPU

TL;DR

This paper introduces a new Bayesian network learning algorithm optimized for GPU, significantly accelerating inference and enabling analysis of larger, more complex networks than previous methods.

Contribution

A novel GPU-accelerated Bayesian network learning algorithm with memory-saving and task assignment strategies, allowing efficient inference on networks with over 60 nodes.

Findings

Achieved 10-fold acceleration per iteration over serial GPP implementation.

Enabled inference on networks with more than 60 nodes.

Incorporated prior information to improve search efficiency.

Abstract

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.