Fast Inference for Probabilistic Graphical Models

Jiantong Jiang, Zeyi Wen, Atif Mansoor, Ajmal Mian

Research output: Chapter in Book/Conference paperConference paperpeer-review

Abstract

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 FastPGM, a fast and parallel PGM inference system for importance sampling-based approximate inference algorithms. FastPGM 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.

Original languageEnglish
Title of host publicationProceedings of the 2024 USENIX Annual Technical Conference, ATC 2024
PublisherUSENIX Association
Pages95-110
Number of pages16
ISBN (Electronic)9781939133410
Publication statusPublished - 2024
Event2024 USENIX Annual Technical Conference - Santa Clara, United States
Duration: 10 Jul 202412 Jul 2024

Conference

Conference2024 USENIX Annual Technical Conference
Abbreviated titleATC 2024
Country/TerritoryUnited States
CitySanta Clara
Period10/07/2412/07/24

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