llm-torch-profiler-analysis

Analyzing torch profiler traces involves inspecting execution profiles to identify GPU performance bottlenecks and kernel fusion opportunities. This process pinpoints slow kernels in LLM serving frameworks to enhance overall throughput.

Yes, you can analyze profiling data from SGLang, vLLM, and TensorRT-LLM frameworks. The approach inspects trace files or directory profiles generated by these specific large language model serving environments to locate performance issues.

Finding kernel fusion opportunities involves inspecting the trace.json.gz file using custom scripts that parse profiling data. This generates detailed tables highlighting fusion failures and execution overlaps to guide optimization.

Yes, Python libraries for trace parsing and custom scripts for report generation are required. These dependencies process profiling data and produce detailed tables highlighting kernels and execution overlaps for optimization.

Slow GPU kernel execution in LLM frameworks stems from performance bottlenecks or fusion failures. Diagnosing it involves generating detailed tables from torch profiling traces to highlight overlapping kernels and pinpoint inefficiencies.