Dart CPU Profiling

Guidelines and automated tools for capturing CPU profiles and identifying bottlenecks in Dart command-line applications.

When to use this skill

• When asked to profile, optimize, or benchmark CPU execution of a Dart script or CLI tool.
• When investigating hot loops, heavy function calls, or unexpected execution overhead.

Workflow

1. Ensure clean compilation: Make sure the target Dart script runs cleanly (dart run <script.dart>).
2. Run Profiler Script: Use the automated profiling helper script inside this skill directory to launch the target app with VM Service observability enabled, capture CPU samples, and output top-consuming functions.
3. Analyze & Optimize: Review the self and total sample percentages reported by the tool to pinpoint bottlenecks (e.g., excessive object allocation, costly hashing, virtual dispatch overhead).

Running the Profiler Helper Script

This repository includes a zero-dependency (using only official vm_service) profiling script that launches any Dart file, connects to the VM Service, waits for execution to complete (--pause-isolates-on-exit), retrieves CPU samples, and prints a clean summary while exporting the full JSON profile.

Run it from any working directory:

dart run <dash_skills_repo>/skills/profile-dart-code/scripts/bin/profile.dart --out=cpu_profile.json -- <path_to_target.dart> [target_arguments...]

Script Arguments

• -o, --out=<file>: Output file path to save the raw JSON CPU profile (default: cpu_profile.json).
• -p, --period=<micros>: Sampling interval in microseconds (default: 1000µs = 1ms). Minimum 50µs.
• -- <target.dart> [args...]: The Dart script to profile, followed by any arguments passed to main().

[!WARNING] Potential Hangs: When profiling or debugging Dart targets using VM services, target exceptions or connection issues can cause the process to hang indefinitely. Ensure your target script handles timeouts, and monitor the process output.

Example Output

Connecting to VM service at ws://127.0.0.1:8181/ws...
Target execution paused at exit. Retrieving CPU profile samples...

=== Top CPU Functions (Self Samples) ===
 1. _PuzzleSmart._shiftSlice (self: 34.2%, total: 41.0%)
 2. _countInversions (self: 18.5%, total: 18.5%)
 3. shortestPaths (self: 12.1%, total: 98.4%)

Saved complete JSON profile to: cpu_profile.json

Best Practices for Interpreting Profiles

1. Focus on Self % vs. Total %: High self % indicates where CPU time is spent directly inside a function's own body (math, loop branching, array indexing). High total % with low self % indicates a dispatcher or outer orchestration loop.
2. Look for Hidden Overhead: Watch out for implicit object allocations (_copyData, iterator wrappers, closure creation) inside tight loops.
3. Verify Optimizations Empirically: Always record baseline sample counts and execution duration (time -v) before and after applying optimizations.