Resilient AI Supercomputer Networking using MRC and SRv6
📰 ArXiv cs.AI
Learn how to build resilient AI supercomputer networks using MRC and SRv6 to reduce tail latency in large-scale synchronous pretraining jobs
Action Steps
- Design a multi-plane Clos topology to achieve high switch radix and redundancy
- Implement MRC, a new RDMA-based transport protocol, to spray traffic across many paths and actively load-balance between them
- Configure SRv6 to enable resilient and efficient routing in the network
- Test the network setup using large-scale synchronous pretraining jobs to measure performance improvements
- Apply the three-pronged approach to existing AI supercomputer networks to reduce tail latency and improve overall performance
Who Needs to Know This
This micro-lesson is suitable for AI engineers, data scientists, and software engineers working on large-scale AI projects, particularly those involved in building and maintaining AI supercomputer networks
Key Insight
💡 MRC and SRv6 can be used together to build resilient AI supercomputer networks that reduce tail latency in large-scale synchronous pretraining jobs
Share This
🚀 Build resilient AI supercomputer networks with MRC and SRv6 to reduce tail latency! 🚀
Key Takeaways
Learn how to build resilient AI supercomputer networks using MRC and SRv6 to reduce tail latency in large-scale synchronous pretraining jobs
Full Article
Title: Resilient AI Supercomputer Networking using MRC and SRv6
Abstract:
arXiv:2605.04333v1 Announce Type: cross Abstract: Tail latency dominates the performance of synchronous pretraining jobs when running at very large scales. We describe a three-pronged approach: (1) a new RDMA-based transport protocol, MRC, sprays across many paths and actively load-balances between them, eliminating the issue of flow collisions (2) the use of multi-plane Clos topologies to get the benefits of high switch radix and redundancy, allowing training clusters well over 100K GPUs to be
Abstract:
arXiv:2605.04333v1 Announce Type: cross Abstract: Tail latency dominates the performance of synchronous pretraining jobs when running at very large scales. We describe a three-pronged approach: (1) a new RDMA-based transport protocol, MRC, sprays across many paths and actively load-balances between them, eliminating the issue of flow collisions (2) the use of multi-plane Clos topologies to get the benefits of high switch radix and redundancy, allowing training clusters well over 100K GPUs to be
DeepCamp AI