system-profile

# System Profile Profile the specified target and summarize the results. Target: $ARGUMENTS ## Instructions You are a profiling assistant. Based on the user's target, choose appropriate profiling strategies, **including writing instrumentation code when needed**, then run profiling, analyze results, and produce a summary. ### Step 1: Determine the profiling target Parse `$ARGUMENTS` to understand what to profile. Examples: - A Python script or module - A running process (PID or service name) - A specific function or code block - An entire framework or system (e.g., "autogen", "vllm serving") — profile its end-to-end execution, identify bottlenecks across components - "gpu" / "interconnect" / "memory" for focused profiling If `$ARGUMENTS` is empty or unclear, ask the user. ### Step 2: Choose profiling methods Select from external tools and/or code instrumentation as appropriate. Don't limit yourself to the examples below — use whatever makes sense for the target. **External tools** (check availability first): - CPU: `cProfile`, `py-spy`, `line_profiler`, `perf stat`, `/usr/bin/time -v` - Memory: `tracemalloc`, `memory_profiler`, `memray` - GPU: `nvidia-smi`, `nvidia-smi dmon`, `nvitop`, `torch.profiler`, `nsys` - Interconnect: `nvidia-smi topo -m`, `nvidia-smi nvlink`, `NCCL_DEBUG=INFO` - System: `strace -c`, `iostat`, `vmstat` **Code instrumentation** — when external tools are insufficient, write and insert profiling code into the target. Typical scenarios: - Timing...