Distributed Deep Learning Training

3.

Model Parallelism

3.1.

Fundamental Concepts

3.1.1.

Model Partitioning Strategies

3.1.1.1.

Layer-wise Partitioning

3.1.1.2.

Tensor-wise Partitioning

3.1.1.3.

Operator-level Partitioning

3.1.2.

Memory Distribution Benefits

3.1.3.

Communication Requirements

3.2.

Pipeline Parallelism

3.2.1.

Layer Pipelining Concepts

3.2.1.1.

Sequential Layer Assignment

3.2.1.2.

Forward Pass Scheduling

3.2.1.3.

Backward Pass Scheduling

3.2.2.

3.2.2.1.

Batch Splitting Strategies

3.2.2.2.

Pipeline Efficiency

3.2.2.3.

Memory Trade-offs

3.2.3.

Pipeline Bubble Problem

3.2.3.1.

Idle Time Analysis

3.2.3.2.

Throughput Impact

3.2.3.3.

Bubble Minimization Techniques

3.2.4.

Advanced Pipeline Techniques

3.2.4.1.

Interleaved Scheduling

3.2.4.2.

Virtual Pipeline Stages

3.2.4.3.

Gradient Accumulation in Pipelines

3.3.

Tensor Parallelism

3.3.1.

Tensor Partitioning Methods

3.3.1.1.

Row-wise Parallelism

3.3.1.2.

Column-wise Parallelism

3.3.1.3.

Block-wise Parallelism

3.3.2.

Matrix Operation Splitting

3.3.2.1.

Matrix Multiplication Partitioning

3.3.2.2.

Element-wise Operation Distribution

3.3.3.

Transformer Model Applications

3.3.3.1.

Self-Attention Parallelization

3.3.3.2.

Feed-Forward Network Splitting

3.3.3.3.

Embedding Table Sharding

3.3.3.4.

Communication Patterns

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4. Hybrid Parallelism Strategies