Parallelism techniques: Quick Reference — GPU Acceleration and Optimization (NVIDIA-Certified Professional: Generative AI LLMs)

Parallelism Techniques Quick Reference for NVIDIA-Certified Professional: Generative AI LLMs

Parallelism is a cornerstone of GPU acceleration and optimization in training large language models (LLMs). Efficient parallelism techniques enable scaling across multiple GPUs and distributed systems, improving training speed and resource utilization.

1. Data Parallelism

• Definition: Each GPU holds a full copy of the model and processes a different subset of the input data (mini-batches).
• Key Feature: Gradients are averaged or summed across GPUs after forward and backward passes.
• Use Case: Effective when model fits into a single GPU’s memory.
• Communication: Requires synchronization of gradients via all-reduce operations.

2. Model Parallelism

• Definition: The model is split across multiple GPUs, each responsible for a portion of the model’s layers or parameters.
• Key Feature: Forward and backward passes are pipelined across GPUs.
• Use Case: Necessary when model size exceeds single GPU memory capacity.
• Communication: Requires passing activations and gradients between GPUs.

3. Pipeline Parallelism

• Definition: Model layers are divided into sequential stages, each assigned to a different GPU; micro-batches flow through the pipeline.
• Key Feature: Enables concurrent execution of different micro-batches on different GPUs.
• Use Case: Combines with model parallelism to improve throughput.
• Considerations: Introduces pipeline bubbles; requires careful scheduling.

4. Tensor Parallelism

• Definition: Individual tensor operations (e.g., matrix multiplications) are split across GPUs.
• Key Feature: Fine-grained parallelism within layers.
• Use Case: Useful for very large layers that cannot fit on a single GPU.

5. Hybrid Parallelism

• Definition: Combines data, model, pipeline, and tensor parallelism to optimize training.
• Key Feature: Balances memory usage, compute efficiency, and communication overhead.
• Example: Data parallelism across nodes + pipeline parallelism within nodes + tensor parallelism within GPUs.

6. Key Rules and Best Practices

• Minimize Communication Overhead: Use efficient collective communication libraries like NCCL.
• Balance Workloads: Ensure even distribution of computation to avoid idle GPUs.
• Optimize Batch Sizes: Larger batches improve GPU utilization but may require gradient accumulation.
• Overlap Computation and Communication: Use asynchronous operations to hide latency.
• Profile and Tune: Use NVIDIA Nsight Systems and nvprof for performance bottleneck identification.

7. Summary Table

Parallelism Type	Description	When to Use
Data Parallelism	Replicate model, split data	Model fits in GPU memory
Model Parallelism	Split model across GPUs	Model too large for one GPU
Pipeline Parallelism	Sequential layer stages	Improve throughput with large models
Tensor Parallelism	Split tensor operations	Very large layers
Hybrid Parallelism	Combine techniques	Maximize efficiency at scale

More in this topic

Memory and batch optimization — GPU Acceleration and Optimization (NVIDIA-Certified Professional: Generative AI LLMs)Parallelism techniques: Common Mistakes — GPU Acceleration and Optimization (NVIDIA-Certified Professional: Generative AI LLMs)Parallelism techniques: Practice Questions — GPU Acceleration and Optimization (NVIDIA-Certified Professional: Generative AI LLMs)Multi-GPU and distributed setups — GPU Acceleration and Optimization (NVIDIA-Certified Professional: Generative AI LLMs)Performance profiling and troubleshooting — GPU Acceleration and Optimization (NVIDIA-Certified Professional: Generative AI LLMs)GPU Acceleration and Optimization — NVIDIA-Certified Professional: Generative AI LLMsParallelism techniques: Worked Example — GPU Acceleration and Optimization (NVIDIA-Certified Professional: Generative AI LLMs)Parallelism techniques — GPU Acceleration and Optimization (NVIDIA-Certified Professional: Generative AI LLMs)