Approaches to Interpreter Composition

TL;DR

This paper evaluates six methods of combining Python and Prolog virtual machines across four configurations, demonstrating that meta-tracing compositions offer faster cross-language execution and lower overhead.

Contribution

It introduces and compares multiple interpreter composition strategies, highlighting the efficiency benefits of meta-tracing in cross-language virtual machine setups.

Findings

Meta-tracing compositions execute cross-language programs faster.

Meta-tracing reduces overhead compared to mono-language execution.

Different composition methods impact performance and overhead.

Abstract

In this paper, we compose six different Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs.