Work

GPU-Accelerated Belief Propagation for Program Analysis

Year: 2026

Type: conference-paper

Abstract: Belief Propagation (BP) is a widely used approximate inference algorithm in probabilistic graphical models (PGMs), but it is computationally expensive when applied to large-scale program analysis. Existing GPU-based approaches are unable to support flexible update strategies and have yet to integrate logical constraints with GPU acceleration, leading to challenges in both generality and efficiency.

Source: IEEE/ACM International Conference on Automated Software Engineering (ASE)

DOI: 10.1145/3832783.3834444

Authors: Haoyu Feng, Xin Zhang

Institution: Peking University

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Natural Science Foundation of China

Award: G5212030523