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 specific code blocks (wall time vs CPU time)
• Measuring CPU-GPU or GPU-GPU transfer size, frequency, and bandwidth
• Tracking memory allocation across CPU and GPU to detect redundancy
• Wrapping NCCL collectives to measure latency and throughput
• Adding CUDA event timing around kernels

Design the instrumentation based on what you observe in the code — don't use a fixed template.

Step 3: Key dimensions to investigate

Depending on the target, focus on some or all of these:

CPU overhead

• Context switching (voluntary / involuntary)
• CPU utilization: ratio of CPU time to wall time
• Per-function execution time hotspots

Memory overhead

• CPU and GPU memory usage (allocated vs reserved vs peak)
• Redundant replication: same data living on both CPU and GPU
• Per-device allocation balance in multi-GPU setups

Interconnect & communication

• CPU-GPU transfer: frequency, per-transfer size, total volume, bandwidth achieved
• GPU-GPU transfer: P2P bandwidth, NVLink vs PCIe topology impact
• NCCL collectives: operation type, message size distribution, latency
• Communication-to-computation ratio

GPU compute

• SM utilization, kernel launch overhead
• Memory bandwidth utilization vs peak

Step 4: Instrumentation guidelines

When inserting code into the target:

1. Read and understand the target code first
2. Prefer wrapping (decorator, context manager, standalone runner) over inline edits
3. If inline edits are necessary, mark them clearly (e.g., # [PROFILE] comments)
4. Minimize observer effect — don't instrument tight inner loops; sample instead
5. Collect results into a structured log, don't scatter print statements

Step 5: Run profiling

1. Check available tools and hardware topology
2. Run the chosen methods, capture all output
3. Save artifacts (flamegraphs, traces, logs) to ./profile_output/

Step 6: Produce the report

Part A — Profiling results (structured tables by dimension, as applicable):

• CPU overhead table
• Memory overhead table (with redundancy column)
• Interconnect table (transfer type / frequency / size / latency / bandwidth)
• Hotspots / bottleneck identification
• Actionable recommendations ranked by expected impact

Part B — Instrumentation changelog (MANDATORY): List every file that was modified or created for profiling purposes:

File	Change type	What was added/modified	Line(s)
...	modified	...	...
...	created	...	—

This allows the user to review and revert all instrumentation changes. Offer to clean up (remove all instrumentation) when the user is done.