Guard: Scalable Straggler Detection and Node Health Management for Large-Scale Training
Training frontier-scale foundation models involves coordinating tens of thousands of GPUs over multi-month runs, where even minor performance degradations can accumulate into substantial efficiency losses. Existing health-check mechanisms, such as NCCL tests or GPU burn-in, primarily focus on functional correctness and often fail to detect fail-slow behaviors that silently degrade system performance. In this paper, we present Guard, a scalable system for detecting stragglers and ensuring node health in large-scale training clusters. Guard combines lightweight online performance monitoring during training with an offline node-sweep mechanism that systematically evaluates and qualifies node...
| Authors | Guanliang Liu, Abhinandan Patni, Congzhu Lin et al. |
|---|---|
| Primary category | cs.DC |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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