Projects per year
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 language | English |
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Title of host publication | Proceedings of the 2024 USENIX Annual Technical Conference, ATC 2024 |
Publisher | USENIX Association |
Pages | 95-110 |
Number of pages | 16 |
ISBN (Electronic) | 9781939133410 |
Publication status | Published - 2024 |
Event | 2024 USENIX Annual Technical Conference - Santa Clara, United States Duration: 10 Jul 2024 → 12 Jul 2024 |
Conference
Conference | 2024 USENIX Annual Technical Conference |
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Abbreviated title | ATC 2024 |
Country/Territory | United States |
City | Santa Clara |
Period | 10/07/24 → 12/07/24 |
Fingerprint
Dive into the research topics of 'Fast Inference for Probabilistic Graphical Models'. Together they form a unique fingerprint.Projects
- 1 Finished
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Defense against adversarial attacks on deep learning in computer vision
Mian, A. (Investigator 01)
ARC Australian Research Council
1/01/19 → 31/03/24
Project: Research
Research output
- 1 Doctoral Thesis
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Efficient automatic probabilistic graphical model learning and inference
Jiang, J., 2024, (Unpublished) 145 p.Research output: Thesis › Doctoral Thesis
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