Introduces a new Python toolset in script/analyze_build/ for analyzing
Clang -ftime-trace JSON output to identify compilation bottlenecks and
optimize C++ metaprogramming build times.
Key features:
- Fast parallel processing of trace json files using all CPU cores (> 100 files/sec)
- Simple, cache-free architecture for consistent performance
- Comprehensive analysis of template instantiations and event types
- Command-line tools and Jupyter notebook support
- Automatic orjson detection for JSON parsing speedup
Components:
- trace_analysis/: Core library (models, parser, transformer)
- examples/: CLI tools for single-file and directory analysis
- notebooks/: Comprehensive Jupyter notebook with analysis patterns
- Detailed README with usage examples and performance data
Also adds ruff configuration to pyproject.toml to ignore E402 (module
level import not at top of file) for Jupyter notebooks, which commonly
have imports after markdown cells.
This toolset addresses the critical problem of long build times in CK's
C++17 metaprogramming codebase by treating -ftime-trace as a big data
problem, using pandas and modern analysis tools to understand compilation
patterns and measure improvement opportunities.
