gpu-memory-model
Installation
SKILL.md
GPU Memory Model
Purpose
Explain the GPU execution and memory model for agents optimizing kernels: SIMT execution, warp (32) vs wavefront (64) divergence costs, global memory coalescing rules, shared memory bank conflicts, L1/L2 cache behavior, atomic memory ordering, and the occupancy-vs-latency-hiding tradeoff.
When to Use
- Diagnosing why a kernel is memory-bound despite high theoretical bandwidth
- Understanding warp divergence from branching
- Fixing shared memory bank conflicts in tiled algorithms
- Choosing block size for occupancy vs register pressure
- Porting kernels between NVIDIA (warp 32) and AMD (wavefront 64)
- Reasoning about atomic contention in parallel reductions