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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
Model Parallelism
Fundamental Concepts
Model Partitioning Strategies
Layer-wise Partitioning
Tensor-wise Partitioning
Operator-level Partitioning
Memory Distribution Benefits
Communication Requirements
Pipeline Parallelism
Layer Pipelining Concepts
Sequential Layer Assignment
Forward Pass Scheduling
Backward Pass Scheduling
Micro-batching
Batch Splitting Strategies
Pipeline Efficiency
Memory Trade-offs
Pipeline Bubble Problem
Idle Time Analysis
Throughput Impact
Bubble Minimization Techniques
Advanced Pipeline Techniques
Interleaved Scheduling
Virtual Pipeline Stages
Gradient Accumulation in Pipelines
Tensor Parallelism
Tensor Partitioning Methods
Row-wise Parallelism
Column-wise Parallelism
Block-wise Parallelism
Matrix Operation Splitting
Matrix Multiplication Partitioning
Element-wise Operation Distribution
Transformer Model Applications
Self-Attention Parallelization
Feed-Forward Network Splitting
Embedding Table Sharding
Communication Patterns
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2. Data Parallelism
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4. Hybrid Parallelism Strategies