CPU Affinity Misconfiguration
CPU affinity misconfiguration prevents DataLoader workers and training threads from using optimal CPU cores.
CPU affinity misconfiguration prevents DataLoader workers and training threads from using optimal CPU cores.
What this failure is
CPU Affinity Misconfiguration is a Performance failure seen during ML training runs. CPU affinity misconfiguration prevents DataLoader workers and training threads from using optimal CPU cores. Common tags: Cpu Affinity, Numa, Data Loader, Performance.
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Why it happens (the mechanism)
Workers and main process on same cores. NUMA nodes not respected. CPU pinning not set. Hyperthreading causes contention. 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
- DataLoader workers compete for same cores
- Training is slower than expected
- CPU utilization is uneven
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| DataLoader slower than expected | Workers and main process on same cores |
| num_workers doesn't help | NUMA nodes not respected |
| High context switch rate | CPU pinning not set |
Which systems are affected
- Multi-core training servers
- NUMA-aware systems
- High-performance data loading
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: DataLoader slower than expected
- ✓Verified signal present: num_workers doesn't help
- ✓Verified signal present: High context switch rate
- ✓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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Root cause
- Workers and main process on same cores
- NUMA nodes not respected
- CPU pinning not set
- Hyperthreading causes contention
The fix and how to prevent it
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