Archive of posts with the tag PyPy

• Apr 28, 2015 Zero-Overhead Metaprogramming

  Runtime metaprogramming and reflection are slow. That’s a common wisdom. Unfortunately. Using refection for instance with Java’s reflection API, its dynamic proxies, Ruby’s #send or #method_missing, PHP’s magic methods such as __call, Python’s __getattr__, C#’s DynamicObjects, or really any metaprogramming abstraction in modern languages unfortunately comes at a price. The fewest language implementations optimize these operations. For instance, on Java’s HotSpot VM, reflective method invocation and dynamic proxies have an overhead of 6-7x compared to direct operations.

• Sep 22, 2014 Are We There Yet? Simple Language-Implementation Techniques for the 21st Century

  The first results of my experiments with self-optimizing interpreters was finally published in IEEE Software. It is a brief and very high-level comparison of the Truffle approach with a classic bytecode-based interpreter on top of RPython. If you aren’t familiar with either of these approaches, the article is hopefully a good starting point. The experiments described in it use SOM, a simple Smalltalk.

• Feb 1, 2014 How to get a JIT Compiler for Free: Implementing SOM Smalltalk with RPython and Truffle

  Today at FOSDEM, I gave a brief talk on implementing SOM, a little Smalltalk, with RPython and Truffle. RPython, probably best known for the PyPy implementation, uses meta-tracing JIT compilation to make simple interpreters fast. Truffle, a research project of Oracle Lab, is an approach for building self-optimizing interpreters and in combination with Graal, it gives a JIT compiler for AST-like interpreters. In the talk, I briefly sketch both of them, without going into many details.