How to Make a Fast Dynamic Language Interpreter
4 hours ago
- #interpreters
- #optimization
- #programming
- A 16.6x speed-up was achieved for the Zef interpreter through a series of 21 optimizations, making it competitive with Lua, QuickJS, and CPython.
- Key techniques included value representation, inline caching, object model redesign, watchpoints, and common sense optimizations.
- The original interpreter was an AST-walking, hashtable-heavy design using Fil-C++, resulting in significant performance penalties.
- Major speed gains came from implementing direct operator calls, symbols for string lookups, and inline caches for dynamic dispatch.
- Optimizations like getter and setter specialization, argument handling improvements, and avoiding std::optional reduced overhead.
- Compiling with Yolo-C++ provided a further 4x speed-up, reaching 67x faster than baseline, though with unsound memory management.
- Benchmark results showed progressive improvements across nbody, splay, richards, and deltablue tests, with final performance close to established interpreters.