RML is a programming language intended for the implementation of Natural Semantics specifications. The basic procedural elements are relations: many-to-many mappings defined by a number of axioms or inference rules. It has control flow, logical variables and (explicit) unification as in Prolog; from ML it borrows a polymorphic type system, data structures, and pattern matching; a facility for separatelycompilable modules also exists. A simple prototype compiler, based on translating RML to Continuation-Passing Style and then to C, has been implemented. Benchmarks indicate that this compiler generates code that is several orders of magnitude faster than Typol, and two times faster than standard Prolog compilers.

Implementing a concurrent programming language such as Java by the means of a translator to an existing language is attractive as it provides portability over all platforms supported by the host language and reduces development time – as many low-level tasks can be delegated to the host compiler. The C and C++ programming languages are popular choices for many language implementations due to the availability of efficient compilers on a wide range of platforms. For garbage-collected languages, however, they are not a perfect match as no support is provided for accurately discovering pointers to heap-allocated data on thread stacks. We evaluate several previously published techniques, and propose a new mechanism, lazy pointer stacks, for performing accurate garbage collection in such uncooperative environments. We implemented the new technique in the Ovm Java virtual machine with our own Java-to-C/C++ compiler using GCC as a back-end compiler. Our extensive experimental results confirm that lazy pointer stacks outperform existing approaches: we provide a speed-up of 4.5 % over Henderson’s accurate collector with a 17 % increase in code size. Accurate collection is essential in the context of real-time systems, we thus validate our approach with the implementation of a real-time concurrent garbage collection algorithm. 1.