Distributed Deep Learning Training

2.

Data Parallelism

2.1.

Fundamental Principles

2.1.1.

Model Replication

2.1.1.1.

Identical Model Copies

2.1.1.2.

Weight Synchronization

2.1.1.3.

Parameter Consistency

2.1.2.

2.1.2.1.

Training Data Partitioning

2.1.2.2.

Batch Distribution

2.1.2.3.

Load Balancing Across Workers

2.2.

Synchronization Strategies

2.2.1.

Synchronous Training

2.2.1.1.

Lock-Step Updates

2.2.1.2.

Global Barrier Synchronization

2.2.1.3.

Consistency Guarantees

2.2.1.4.

Straggler Problem

2.2.1.4.1.

Slow Worker Impact

2.2.1.4.2.

Detection Methods

2.2.1.4.3.

Mitigation Strategies

2.2.2.

Asynchronous Training

2.2.2.1.

Independent Worker Updates

2.2.2.2.

Stale Gradient Handling

2.2.2.3.

Convergence Considerations

2.2.2.4.

Parameter Staleness Effects

2.3.

Gradient Aggregation

2.3.1.

Centralized Aggregation

2.3.1.1.

Parameter Server Communication

2.3.1.2.

Bottleneck Analysis

2.3.1.3.

Scalability Limitations

2.3.2.

Decentralized Aggregation

2.3.2.1.

All-Reduce Operations

2.3.2.2.

Ring-Based Communication

2.3.2.3.

Tree-Based Communication

2.3.2.4.

Bandwidth Efficiency

2.4.

Large-Batch Training

2.4.1.

Scaling Challenges

2.4.1.1.

Generalization Gap

2.4.1.2.

Optimization Instability

2.4.1.3.

Gradient Noise Reduction

2.4.2.

Scaling Techniques

2.4.2.1.

Linear Learning Rate Scaling

2.4.2.2.

Learning Rate Warmup

2.4.2.3.

Layer-wise Adaptive Rate Scaling

2.4.2.4.

Gradient Clipping

2.4.2.5.

Batch Size Scheduling

Previous

1. Introduction to Distributed Deep Learning

Go to top

Next

3. Model Parallelism