Practical Partial Evaluation for High-Performance Dynamic Language Runtimes Thomas Wurthinger¨ ∗ Christian Wimmer∗ Christian Humer∗ Andreas Woߨ ∗ Lukas Stadler∗ Chris Seaton∗ Gilles Duboscq∗ Doug Simon∗ Matthias Grimmery ∗ y Oracle Labs Institute for System Software, Johannes Kepler University Linz, Austria fthomas.wuerthinger, christian.wimmer, christian.humer, andreas.woess, lukas.stadler, chris.seaton, gilles.m.duboscq,
[email protected] [email protected] Abstract was first implemented for the SELF language [23]: a multi- Most high-performance dynamic language virtual machines tier optimization system with adaptive optimization and de- duplicate language semantics in the interpreter, compiler, optimization. Multiple tiers increase the implementation and and runtime system, violating the principle to not repeat maintenance costs for a VM: In addition to a language- yourself. In contrast, we define languages solely by writ- specific optimizing compiler, a separate first-tier execution ing an interpreter. Compiled code is derived automatically system must be implemented [2, 4, 21]. Even though the using partial evaluation (the first Futamura projection). The complexity of an interpreter or a baseline compiler is lower interpreter performs specializations, e.g., augments the in- than the complexity of an optimizing compiler, implement- terpreted program with type information and profiling infor- ing them is far from trivial [45]. Additionally, they need to mation. Partial evaluation incorporates these specializations. be maintained and ported to new architectures. This makes partial evaluation practical in the context of dy- But more importantly, the semantics of a language need namic languages, because it reduces the size of the compiled to be implemented multiple times in different styles: For code while still compiling in all parts of an operation that are the first-tier interpreter or baseline compiler, language op- relevant for a particular program.