Parallelism Comparison: Data Parallelism vs Expert Parallelism
This image compares two major parallelization strategies used for training large language models (LLMs).
Left: Data Parallelism
Structure:
- Data is divided into multiple batches from the database
- Same complete model is replicated on each GPU
- Each GPU independently processes different data batches
- Results are aggregated to generate final output
Characteristics:
- Scaling axis: Number of batches/samples
- Pattern: Full model copy on each GPU, dense training
- Communication: Gradient All-Reduce synchronization once per step
- Advantages: Simple and intuitive implementation
- Disadvantages: Model size must fit in single GPU memory
Right: Expert Parallelism
Structure:
- Data is divided by layers
- Tokens are distributed to appropriate experts through All-to-All network and router
- Different expert models (A, B, C) are placed on each GPU
- Parallel processing at block/thread level in GPU pool
Characteristics:
- Scaling axis: Number of experts
- Pattern: Sparse structure – only few experts activated per token
- Goal: Maintain large capacity while limiting FLOPs per token
- Communication: All-to-All token routing
- Advantages: Can scale model capacity significantly (MoE – Mixture of Experts architecture)
- Disadvantages: High communication overhead and complex load balancing
Key Differences
| Aspect | Data Parallelism | Expert Parallelism |
|---|---|---|
| Model Division | Full model replication | Model divided into experts |
| Data Division | Batch-wise | Layer/token-wise |
| Communication Pattern | Gradient All-Reduce | Token All-to-All |
| Scalability | Proportional to data size | Proportional to expert count |
| Efficiency | Dense computation | Sparse computation (conditional activation) |
These two approaches are often used together in practice, enabling ultra-large-scale model training through hybrid parallelization strategies.
Summary
Data Parallelism replicates the entire model across GPUs and divides the training data, synchronizing gradients after each step – simple but memory-limited. Expert Parallelism divides the model into specialized experts and routes tokens dynamically, enabling massive scale through sparse activation. Modern systems combine both strategies to train trillion-parameter models efficiently.
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Lechuck Park 