Legion - Automatically Pushing the Envelope of Multi-GPU System for Billion-Scale GNN Training

Overview

Join us to delve into Legion, an innovative system tailored to streamline and enhance billion-scale GNN training. By leveraging state-of-the-art multi-GPU caching and hierarchical partitioning, Legion offers an automated and efficient approach that significantly outperforms current technologies.

Perfect for those interested in artificial intelligence and seeking to advance their understanding of large-scale neural network training.

Syllabus

• Introduction to Legion
Overview of Multi-GPU Systems
Challenges in Billion-Scale GNN Training
• Fundamentals of Graph Neural Networks (GNNs)
Basics of GNN Architecture
Applications of GNNs in Various Domains
• Multi-GPU System Architecture
GPU Basics and Parallel Processing
Multi-GPU System Integration and Communication
• Advanced Techniques in GNN Training
Hierarchical Partitioning Strategies
Multi-GPU Caching Mechanisms
Data Sharding and Distribution
• Automatic Management in GNN Systems
Resource Allocation and Load Balancing
Dynamic Scaling and Optimization
• Legion System Design
Core Principles of Legion
Architectural Components and Innovations
• Comparative Analysis
Performance Metrics and Benchmarks
Comparison with Existing GNN Training Solutions
• Practical Implementation
Setting up a Multi-GPU Environment
Hands-On with Legion: Installation and Configuration
Running a GNN Training Session with Legion
• Case Studies and Real-World Applications
Successful Deployments of Legion
Insights and Lessons Learned
• Future Directions in Multi-GPU GNN Training
Emerging Trends and Technologies
Research Opportunities and Challenges
• Course Wrap-up and Evaluation
Summary and Key Takeaways
Final Assessment and Feedback

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Subjects

Conference Talks