Pipeline Gpu Kernel
技能 已验证 活跃Apply software pipelining (double-buffering) to a tiled GPU kernel to overlap global memory loads with Tensor Core computation. Covers prologue/loop/epilogue restructuring, LDG-register vs cp.async (LDGSTS) variant selection based on compute/load ratio, shared memory budget verification against architecture-specific occupancy cliffs, and SASS-level verification of load/compute overlap.
Optimize GPU kernel performance by implementing advanced software pipelining techniques to effectively overlap memory operations with computation.
功能
- Software pipelining for GPU kernels
- Double-buffering of shared memory
- Variant selection based on compute/load ratio
- Analysis of load/compute overlap in SASS
- Shared memory budget verification against occupancy cliffs
使用场景
- When a GPU kernel is identified as memory-bound.
- When warp interleaving alone is insufficient to hide DRAM latency.
- When restructuring a sequential load-sync-compute-sync kernel loop.
- When needing to optimize Tensor Core computation by overlapping memory loads.
非目标
- Optimizing kernels that are not memory-bound.
- Addressing bottlenecks unrelated to memory loads or Tensor Core computation.
- Applying pipelining to kernels without a distinct load and compute phase.
- Basic CUDA compilation; assumes familiarity with `nvcc` and GPU architectures.
Practical Utility
- info:Usage examplesWhile the SKILL.md provides detailed procedural steps, it lacks concrete end-to-end invocation examples with specific inputs and expected outputs for the CUDA kernel optimization.
安装
/plugin install agent-almanac@pjt222-agent-almanac质量评分
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