Fast Inference for Probabilistic Graphical Models

Probabilistic graphical models (PGMs) have attracted much attention due to their firm theoretical foundation and inherent interpretability. However, existing PGM inference systems are inefficient and lack sufficient generality, due to issues with irregular memory accesses, high computational complexity, and modular design limitation. In this paper, we present Fast-PGM, a fast and parallel PGM inference system for importance sampling-based approximate inference algorithms. Fast-PGM incorporates careful memory management techniques to reduce memory consumption and enhance data locality. It also employs computation and parallelization optimizations to reduce computational complexity and improve the overall efficiency. Furthermore, Fast-PGM offers high generality and flexibility, allowing easy integration with all the mainstream importance sampling-based algorithms. The system abstraction of Fast-PGM facilitates easy optimizations, extensions, and customization for users. Extensive experiments show that Fast-PGM achieves 3 to 20 times speedup over the state-of-the-art implementation. Fast-PGM source code is freely available at https://github.com/jjiantong/FastPGM.