Resilient AI Supercomputer Networking using MRC and SRv6#
@Scale: Networking 2026 digest Program Core pick 25 August 2026
Jitendra Padhye (OpenAI)
- Why: Tail latency — not average throughput — is what stalls synchronous pretraining at frontier scale, and one colliding flow or one failed link can throttle a job of tens of thousands of accelerators. This is OpenAI's production answer, and it is a transport-plus-routing co-design rather than a tweak to either.
- What: MRC, a new RDMA-based transport that sprays across many paths and actively load-balances between them, eliminating flow collisions; paired with static SRv6 source routing that lets the transport bypass failures on its own. Run in production at OpenAI to train frontier models, where MRC lets training jobs ride out network failures that previously would have interrupted the run. (The abstract calls this a three-pronged approach but enumerates only two prongs; the third is not specified in the program text.) (from abstract only)
- How: Path spraying removes the ECMP flow-collision problem by not pinning flows to single paths; active load balancing keeps utilization even as paths churn. SRv6 segment lists encode the path, so on failure the endpoints — not the routing protocol — select alternates: failover without reconvergence.
- Where applicable: OpenAI's production training clusters, org-specific at extreme scale. The pattern (spray plus endpoint-controlled, source-routed failover over richly connected Ethernet) is broadly transferable, but it presumes a multipath topology and an SRv6-capable fabric.