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1. IL-0421 Errors, mostly GPU faults, crashed Alibaba LLM training jobs as NCCL errors; a job lost 31.19% of its time in June 2023

   Alibaba Group2025

   Symptoms

   Large LLM training jobs on Alibaba's AI clusters crashed again and again and had to restart from the last valid checkpoint. One of the largest jobs, on 4,096 GPUs in a newly deployed cluster, crashed 40 times in one month, and most crashes reached users only as "NCCL Errors".

   Training job failureNCCL timeoutGPU unavailable

   Root cause

   Hardware failure

   Component

   GPU hardware and memoryNCCLPyTorch

   Contributing factor

   Monitoring blind spotUnverified assumption

   Detection

   Before C4D, users found crashes through NCCL errors and could stay unaware of a stalled job for up to 30 minutes until the PyTorch elastic agent killed it; C4D later detects hangs and slowdowns from collective communication timings within tens of seconds.

   Remediation

   • Allocated 64 backup GPUs on 8 servers for every 1024 GPUs on 128 servers, so that parallel training on any 128 servers of the 136-server pool keeps the same communication and performance
   • Deployed C4D to detect faulty nodes automatically, isolate them and restart the job from the most recent valid checkpoint
   • Saved checkpoints more often, every 10 minutes, to cut the work lost after each crash
   • Enhanced cooling with higher fan speeds and better air conditioning to keep GPUs from overheating
   • Identified and enhanced the most vulnerable components of the system, lowering the average error rate 3.33 times

   Action items

   • Take load variation into account in C4D for more precise slow detection

   Lessons

   Most crash causes are confined to specific nodes or devices, so isolating the faulty component quickly and restarting, while leaving root cause analysis for offline work, removes most of the error-induced downtime. Because synchronous training makes many different faults look like the same communication error, diagnosis has to use the regular rhythm of the workers at synchronisation points rather than the error message, and frequent checkpoints matter because error rates in large clusters are high.

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2. IL-0249 Large training jobs on a cluster of over 10,000 GPUs slowed for tens of seconds to nearly ten hours without crashing

   Alibaba Group2024

   Symptoms

   On a shared production cluster of over 10,000 GPUs, large hybrid-parallel training jobs kept running but slowed down, sometimes for hours, without crashing. Among 27 jobs of 512 to 1024 GPUs each submitted in July 2024, 16 slowed this way for 72 minutes on average, and their job completion time grew by 34.59% on average.

   Throughput degradation

   Root cause

   Capacity exhaustion

   Component

   InfiniBand fabricGPU hardware and memory

   Contributing factor

   Overlapping failuresMonitoring blind spot

   Detection

   The authors found fail-slows with small probe training jobs that tracked their own performance at runtime, and by inspecting by hand a one-month trace of large training jobs.

   Remediation

   • Found fail-slows by hand and handled them as fail-stops with checkpoint-and-restart

   Action items

   • Detect fail-slows at runtime from iteration times and locate the slow GPUs and links with Falcon-Detect
   • Mitigate fail-slows with Falcon-Mitigate, escalating from cheap strategies to checkpoint-and-restart only if the slowdown persists

   Lessons

   Fail-slows are usually transient and come from computation, through slow GPUs or CPU contention, or from communication, mostly network congestion. Computation fail-slows are rarer and shorter and cost less, while cross-node communication fail-slows are more common, last longer and slow training more. As training grows, several performance problems are more likely to occur at once, and together they can slow training by more than 90%.

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