kernel-trace-analysis

Profile GPU kernels by collecting instruction-level execution traces with rocprofv3. This analyzes barrier stalls, idle cycles, and memory-bound operations to pinpoint exact performance bottlenecks.

Instruction-level trace analysis for ROCm kernels captures detailed execution statistics using rocprofv3. It examines specific instructions causing stalls and idle cycles to generate a prioritized optimization plan.

Generate an optimization plan by profiling the slow GPU kernel execution to collect instruction traces. The analysis identifies stall-causing instructions and provides concrete, actionable steps to resolve bottlenecks.

Yes, rocprofv3 analyzes memory-bound operations by collecting detailed performance statistics and instruction-level traces. It specifically detects memory-bound issues alongside barrier stalls and idle cycles.

Yes, GPU kernel profiling supports standard Python benchmarking commands. You can profile executions like 'python bench_pa.py --batch 32' directly to collect the necessary performance traces.

GPU kernel trace analysis detects barrier stalls, idle cycles, and memory-bound operations. It profiles these execution bottlenecks at the instruction level to guide targeted kernel tuning.