CHAPTER 4.3 · DISTRIBUTED ATTENTION & RING TOPOLOGY

Ring Topology for Distributed Attention

How Ring Attention processes large sequences across multiple devices without communication becoming a bottleneck – Overlap of Compute and Communication

Ring Attention solves the sequence length problem at the hardware level: Instead of overloading one GPU with 1M tokens, the sequence is distributed across 8-16 GPUs. The trick: While one GPU computes, it simultaneously sends KV data to the next – no waiting for communication.

📖 Learning Context ▼
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Learning Objectives

  • Understand the ring communication pattern for distributed attention
  • Comprehend how compute and communication overlap
  • Recognize why Ring Attention enables near-linear speedup
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Context: Where are we?

Step 4/6 Optimizations & Memory

Ring Attention extends Sliding Window to multi-GPU settings. Together they enable context windows of 1M+ tokens – far beyond what a single GPU can process.

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Why it matters

Gemini's 1M context and Claude's 200K use distributed attention. Without ring topology, communication between GPUs would be the bottleneck – each GPU would have to wait for all others.

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Key Takeaways

  • Sequence Parallelism: Each GPU holds only 1/N of the sequence, but all KV rotate in the ring
  • Overlap: GPU i computes with KV from GPU i-1, while i-1 is already sending to i+1
  • Scaling: 8 GPUs = 8x more context with nearly 8x speedup
Devices
4
Sequence Length Total
256K
Block/Device
64K
Communication Vol
128 GB
Compute-Comm Overlap
92%
Effective Context
256K Tokens
Fig. 1 | Ring Topology with 4-16 GPUs. Each GPU computes attention over its KV block while KV blocks circulate. Colors: Green=Compute Phase, Orange=Communication Phase. The overlap prevents communication from blocking the critical path.
Metric 4 GPUs 8 GPUs 16 GPUs Trend
Sequence Length/GPU 64K 32K 16K Linear with Devices
Total Sequence 256K 256K 256K Constant (scalable)
Local Attention Ops O(64K²) O(32K²) O(16K²) Quadratic per GPU
Communication Volume 512 GB (per phase) 512 GB (per phase) 512 GB (per phase) Constant (good!)
Phases 4 8 16 N = # Devices
Comm Latency ~80ms (NVLink) ~80ms ~80ms Constant (Ring)
Compute Time/Phase ~200ms ~50ms ~12ms Quadratic↓
Overlap % 88% 92% 96% Better with more GPUs
Effective Speedup 3.8× 7.2× 14.1× Near linear!
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Ring Topology Scales Linearly
Sequence length per device decreases with 1/N, but total scales linearly. N GPUs can process 256K tokens with near 14× speedup at 16 devices. Key: Communication constant, computation scales.
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Compute-Communication Overlap is Critical
GPU i computes KV₁ attention (Compute) while GPU i-1 sends KV₁ (Comm). Circulation happens in parallel with computation. Without overlap: only 50% GPU utilization. With overlap: 92-96% possible.
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Phase Structure: N Phases per Circulation
With 4 GPUs: 4 phases until all K-V blocks have circulated. Phase i: GPU j computes with KV₍ⱼ₊ᵢ₎ mod N. After phase N, all attention scores are computed. Deterministic and synchronizable.
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Communication Volume Remains Constant
Per phase: ~512 GB (KV blocks circulate). Regardless of 4 or 16 GPUs. This is different from tree-reduction (would be logarithmic). Ring topology is ideal for attention workloads.
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Practically Limited by NVLink Bandwidth
H100 NVLink: 141 GB/s. 512 GB transfer takes ~3.6s. With good compute/comm overlap, phase takes ~200ms at 4 GPUs. Scales with more GPUs as compute time decreases faster than comm time.
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8-16 GPUs are the Sweet Spot
4 GPUs: 88% overlap, 3.8× speedup. 16 GPUs: 96% overlap, 14.1× speedup. Beyond 16 GPUs: Network switching needed (instead of NVLink), latency increases. Intra-node: Ring optimal.