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Computer Science
Artificial Intelligence
Deep Learning
Distributed Deep Learning Training
1. Introduction to Distributed Deep Learning
2. Data Parallelism
3. Model Parallelism
4. Hybrid Parallelism Strategies
5. Communication in Distributed Training
6. Communication Optimization
7. System and Hardware Considerations
8. Frameworks and Libraries
9. Performance Optimization and Tuning
10. Practical Implementation
11. Advanced Topics and Future Directions
4.
Hybrid Parallelism Strategies
4.1.
Multi-Dimensional Parallelism
4.1.1.
Combining Parallelism Types
4.1.2.
Device Topology Considerations
4.1.3.
Communication Optimization
4.2.
2D Parallelism
4.2.1.
Data and Tensor Parallelism Combination
4.2.2.
Device Mesh Configuration
4.2.3.
Communication Pattern Analysis
4.3.
3D Parallelism
4.3.1.
Data Parallelism Dimension
4.3.2.
Pipeline Parallelism Dimension
4.3.3.
Tensor Parallelism Dimension
4.3.4.
Workload Balancing
4.3.5.
Optimal Configuration Selection
4.4.
Expert Parallelism
4.4.1.
Mixture of Experts Models
4.4.2.
Expert Routing Strategies
4.4.3.
Load Balancing Techniques
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3. Model Parallelism
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5. Communication in Distributed Training