Distributed PyTorch training on Intel hardware
Summary
The article combines Transformers, oneCCL and Intel extensions for distributed CPU training. It shows that acceleration is not limited to GPUs but requires careful software tuning. oneCCL provides optimised collective communication for Intel systems.
Ideas
- For certain models, CPU clusters can become competitive through optimised collectives and vector instructions.
- Distributed training is shaped by communication and memory access as much as by computing power.
Insights
- Scalable training needs reproducible transitions between code, data, devices and checkpoints.
Facts
- Intel Extension for PyTorch adds hardware-specific operator optimisations.
Critique
- Results on Intel test systems cannot be transferred to other processors without measuring.
Recommendations
- Compare total cost and scaling efficiency, not just the runtime of a single node.
References
Read the original article on Hugging Face
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