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Silent Data Corruption

Silent data corruption during training causes wrong results without error messages, often from hardware issues.

Quick answer

Silent data corruption during training causes wrong results without error messages, often from hardware issues.

Reliability#silent-corruption#bit-flip#ecc#reliability#integrity

What this failure is

Silent Data Corruption is a Reliability failure seen during ML training runs. Silent data corruption during training causes wrong results without error messages, often from hardware issues. Common tags: Silent Corruption, Bit Flip, Ecc, Reliability.

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Why it happens (the mechanism)

GPU memory bit flips (cosmic rays, hardware issues). Storage bit flips in checkpoint. CPU memory errors. Network bit flips in distributed training. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

  • Training loss is wrong but no error
  • Model accuracy is poor without obvious cause
  • Results are non-reproducible across runs

Common symptoms and what they mean

SymptomWhy it happens
Same code gives different resultsGPU memory bit flips (cosmic rays, hardware issues)
Bit flips in memory or storageStorage bit flips in checkpoint
Some samples produce different outputsCPU memory errors

Which systems are affected

  • Long-running training
  • Large-scale GPU training
  • High-performance computing

How to confirm this is the problem

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

  • ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
  • ✓Verified signal present: Same code gives different results
  • ✓Verified signal present: Bit flips in memory or storage
  • ✓Verified signal present: Some samples produce different outputs
  • ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

The fix and the prevention pattern

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Related failures to investigate next

Bytedance Sequence Length Straggler often co-occurs with this failure because both share underlying assumptions about reliability; if you fix this entry but still see drift, validate the related primary cause next. bytedance sequence length straggler →
Training Checkpoint Corruption On Write often co-occurs with this failure because both share underlying assumptions about reliability; if you fix this entry but still see drift, validate the related neighboring cause next. training checkpoint corruption on write →
Checkpoint Partial Save often co-occurs with this failure because both share underlying assumptions about reliability; if you fix this entry but still see drift, validate the related neighboring cause next. checkpoint partial save →

Root cause

  • GPU memory bit flips (cosmic rays, hardware issues)
  • Storage bit flips in checkpoint
  • CPU memory errors
  • Network bit flips in distributed training

The fix and how to prevent it

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References

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