kernel profiling

**Kernel profiling** is the **fine-grained analysis of GPU kernel execution behavior, efficiency, and stall causes** - it reveals low-level performance limits that are not visible in high-level operator summaries. **What Is Kernel profiling?** - **Definition**: Measurement of per-kernel occupancy, instruction throughput, memory traffic, and stall breakdown. - **Diagnostic Signals**: Compute utilization, cache hit rates, warp stalls, and tensor-core engagement. - **Tooling**: Often performed with Nsight Compute and framework-linked kernel attribution data. - **Outcome**: Precise bottleneck classification guiding kernel fusion, tiling, and memory-access redesign. **Why Kernel profiling Matters** - **Precision Tuning**: Kernel-level insight is needed to unlock advanced hardware performance potential. - **Bottleneck Isolation**: Separates memory-bound, compute-bound, and latency-bound kernels clearly. - **Optimization Verification**: Confirms whether code changes improve the intended microarchitectural metric. - **Scale Impact**: Small kernel gains can produce large aggregate speedups in repeated training loops. - **Regression Defense**: Kernel profiles detect subtle degradations after compiler or library updates. **How It Is Used in Practice** - **Hotspot Focus**: Profile top runtime kernels first to maximize optimization return. - **Metric Correlation**: Interpret occupancy and bandwidth counters together rather than in isolation. - **Iteration**: Apply targeted changes and re-profile until stall reasons and throughput meet targets. Kernel profiling is **the microscope of GPU performance engineering** - deep kernel evidence is required to convert framework-level speed goals into sustained hardware efficiency.