Deep Dive into TPU and GPU Cluster Communication
gordic_aleksa · x · 2026-07-15
This extensive article explores the collective communication primitives within TPU and GPU clusters and how they support large model training and inference scaling.
Key topics covered include:
- TPU cluster topology: superpods, slices, DCN, PCIe, ICI
- Core communication operators: All-Gather, Reduce-Scatter, All-Reduce, All-to-All
- The All-to-All dependency for token distribution in MoE
- GPU cluster topology: DGX reference architecture, nodes, fat trees, scalable units
- Intra- and inter-node communication algorithms: rings, trees, SHARP, hierarchical collective communication, InfiniBand
The author emphasizes that truly understanding scaling for training/inference requires looking beyond FSDP, expert parallelism, or data parallelism to grasp the underlying hardware topology and communication algorithms.
Related event: Deep Dive on Collective Communication in TPU/GPU Clusters Gains Traction(5 posts)→
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