Distributed Deep Learning Training

5.

Communication in Distributed Training

5.1.

Communication Fundamentals

5.1.1.

Latency Characteristics

5.1.2.

Bandwidth Requirements

5.1.3.

Communication Volume Analysis

5.1.4.

Computation-to-Communication Ratio

5.2.

Collective Communication Primitives

5.2.1.

Point-to-Point Operations

5.2.1.1.

Send and Receive

5.2.1.2.

Blocking vs Non-blocking

5.2.2.

One-to-Many Operations

5.2.2.1.

5.2.2.2.

5.2.3.

Many-to-One Operations

5.2.3.1.

5.2.3.2.

5.2.4.

Many-to-Many Operations

5.2.4.1.

5.2.4.2.

5.2.4.3.

5.2.4.4.

5.3.

Communication Topologies

5.3.1.

5.3.1.1.

Communication Patterns

5.3.1.2.

Scalability Properties

5.3.2.

5.3.2.1.

Hierarchical Communication

5.3.2.2.

Latency Characteristics

5.3.3.

5.3.3.1.

Direct Connections

5.3.3.2.

Bandwidth Utilization

5.4.

Communication Backends

5.4.1.

NVIDIA Collective Communications Library

5.4.1.1.

GPU Optimization

5.4.1.2.

Multi-GPU Support

5.4.1.3.

Network Integration

5.4.2.

5.4.2.1.

CPU and GPU Support

5.4.2.2.

Cross-Platform Compatibility

5.4.3.

Message Passing Interface

5.4.3.1.

Standardized Protocols

5.4.3.2.

High-Performance Computing Integration

5.4.4.

Backend Selection Criteria

5.4.4.1.

Hardware Compatibility

5.4.4.2.

Performance Benchmarking

5.4.4.3.

Feature Requirements

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6. Communication Optimization