Individual skill
gpu-memory-model
Use when analyzing warp divergence, memory coalescing, shared memory bank conflicts, cache behavior, atomics, or occupancy tradeoffs on NVIDIA and AMD GPUs. Not for tool commands: use cuda-profiling.
.devin/skills/gpu-memory-model/SKILL.md
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- Observed behavior
- Instructions only
- Last checked
- Nov 4, 2026
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- Repository last updated
- Sep 28, 2026
- License
- Apache-2.0
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SKILL.mdcore · 7,054 bytesagents/openai.yamlasset · 269 bytes
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# GPU memory model
## Contract
| Field | Bound contract |
|---|---|
| Trigger | Kernel behavior needs explaining or predicting through the SIMT execution model and the memory hierarchy: divergence costs, coalescing rules, bank conflicts, cache levels, atomic contention, or occupancy versus latency hiding. |
| Authority | Read-only. The skill emits explanations and code patterns; it writes no files. Nothing to roll back. No remote mutation. |
| Side effect | Explanations, access-pattern rewrites, and metric names to confirm each hypothesis. |
| Done | The observed behavior is derived from the model, and a profiler metric that can confirm or refute it is named. |
## Inputs
1. Kernel or pattern (required): the code whose behavior needs explaining.