Debugging Billion-Core AI Clusters: Distributed Crash Diagnosis for Heterogeneous Accelerator Systems

Abstract

Correlated kernel panics are a common distributed system reliability issue because modern AI systems involve billions of logical processing cores on a variety of NVIDIA, TPU, AMD, and specialized accelerator platforms. This study examined Cluster Crash Diagnosis (CCD) as an architecture that uses Linux kdump technology and transforms it into a scalable, secure fleet telemetry system. The study involved two production validations, using an 8,000-node NVIDIA H100 cluster and a 512-node TPU v5e pod, respectively. CCD introduced 2 GB of crashkernel reservation, vendor sidecars, local persistence of vmcore, LZO compression, 50 Mbps transport control, exponential backoff, hashing stack with a hash function that uses SHA256, normalized Levenshtein clustering at a threshold of τ = 0.85, vendor-neutral classification, and topology-aware analytics. In the H100 case, the crash rate went up from 0.5 crashes/day to 15 crashes/day after a patch to the operating system; CCD reduced diagnosis time from 6 hours to 8 minutes, a 45× faster turnaround. In the TPU case, all crashes observed were associated with the same failing top-of-rack optical switch. Overall, CCD lowered the MTTD by 70-85% while improving multi-tenant security. These results demonstrate how coordinated capture, transport, deduplication, correlation, and access controls can help enhance resilience in exascale AI operations. Future work should extend predictive topology correlation and cross-vendor validation.