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Fast Inference for Probabilistic Graphical Models

Offered By: USENIX via YouTube

Tags

Probabilistic Graphical Models Courses Memory Management Courses Parallel Computing Courses Importance Sampling Courses

Course Description

Overview

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Explore a conference talk on Fast-PGM, an innovative system for fast and parallel inference in probabilistic graphical models (PGMs). Delve into the challenges faced by existing PGM inference systems, including inefficiency and lack of generality. Learn about Fast-PGM's approach to overcoming these issues through careful memory management techniques, computation and parallelization optimizations, and a flexible system design. Discover how Fast-PGM achieves significant speedups over state-of-the-art implementations while maintaining high generality and ease of integration with mainstream importance sampling-based algorithms. Gain insights into the system's architecture, which facilitates easy optimizations, extensions, and customization for users. Understand the potential impact of Fast-PGM on improving the efficiency and applicability of PGMs in various domains.

Syllabus

USENIX ATC '24 - Fast Inference for Probabilistic Graphical Models


Taught by

USENIX

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