How to Make a Fast Dynamic Language Interpreter
The creator of the Zef dynamic language meticulously chronicles how they optimized its simple AST-walking interpreter from scratch. They detail a series of 21 fundamental changes, like value representation and inline caching, achieving a 16.6x speed-up. This detailed account offers practical insights into interpreter performance, demonstrating significant gains without relying on complex JIT compilers.
The Lowdown
This post provides a comprehensive, step-by-step guide to optimizing a simple Abstract Syntax Tree (AST)-walking interpreter for Zef, a dynamic language created by the author. Unlike most discussions on language performance that focus on Just-In-Time (JIT) compilation or advanced garbage collectors, this article highlights techniques applicable when starting from a basic interpreter, demonstrating how massive speed improvements can be achieved through fundamental changes to its core components.
- The original Zef interpreter was built for simplicity with little regard for performance, employing recursive AST walking,
std::stringeverywhere, and heavy use ofstd::unordered_mapfor lookups, making it significantly slower (35-80x) than CPython, Lua, and QuickJS-ng. - The author systematically introduced 21 distinct optimizations, ranging from directly calling operators and avoiding
IntObjectchecks to implementing hash-consed symbols, a revamped object model with inline caches and watchpoints, and specialized argument handling. - Key performance-critical areas targeted included method dispatch, value representation, memory allocation strategies (especially in the context of Fil-C++'s behavior), and reducing string-based lookups.
- Each optimization's impact is quantified, cumulatively resulting in a 16.6x speed-up when compiled with Fil-C++.
- Finally, compiling the optimized Zef interpreter with a 'Yolo-C++' (standard GCC C++) yielded an additional 4x speed-up, making it competitive with, and even faster than, highly optimized interpreters like CPython and QuickJS-ng, despite this configuration being unsound due to lack of a proper garbage collector.
- The entire optimization process was rigorously benchmarked using a custom suite,
ScriptBench1, which includes ports of classic language benchmarks like Richards, DeltaBlue, N-Body, and Splay.
The narrative showcases that a deep understanding of interpreter mechanics and careful, iterative optimization of core components can lead to dramatic performance improvements, even for an interpreter built with expediency and simplicity as initial priorities, effectively bridging the gap to more mature language runtimes.