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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
Communication in Distributed Training
Communication Fundamentals
Latency Characteristics
Bandwidth Requirements
Communication Volume Analysis
Computation-to-Communication Ratio
Collective Communication Primitives
Point-to-Point Operations
Send and Receive
Blocking vs Non-blocking
One-to-Many Operations
Broadcast
Scatter
Many-to-One Operations
Gather
Reduce
Many-to-Many Operations
All-Gather
All-Reduce
All-to-All
Reduce-Scatter
Communication Topologies
Ring Topology
Communication Patterns
Scalability Properties
Tree Topology
Hierarchical Communication
Latency Characteristics
Mesh Topology
Direct Connections
Bandwidth Utilization
Communication Backends
NVIDIA Collective Communications Library
GPU Optimization
Multi-GPU Support
Network Integration
Gloo Backend
CPU and GPU Support
Cross-Platform Compatibility
Message Passing Interface
Standardized Protocols
High-Performance Computing Integration
Backend Selection Criteria
Hardware Compatibility
Performance Benchmarking
Feature Requirements
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4. Hybrid Parallelism Strategies
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6. Communication Optimization