Most Prolog implementations are implemented in low-level languages such as C and are based on a variation of the WAM instruction set, which enhances their performance but makes them hard to write. We present a high-level continuation-based Prolog interpreter written in RPython, a restricted subset of Python. This interpreter is annotated with hints, so that it can be fed through the PyPy tracing JIT generator, which incorporates partial evaluation techniques. The resulting Prolog implementation is surprisingly efficient: it clearly outperforms existing implementations of Prolog in high-level languages such as Java. Moreover, on some benchmarks, our system outperforms state-of-the-art WAM-based Prolog implementations. Our paper tries to show that PyPy can indeed form the basis for implementing programming languages other than Python. Furthermore, we believe that our results showcase the great potential of the tracing JIT approach for declarative programming languages

In this paper we present a Prolog plugin for Eclipse based upon BE4, and providing many features such as semantic-aware syntax highlighting, outline view, error marking, content assist, hover information, documentation generation, and quick fixes. The plugin makes use of a Java parser for full Prolog with an inte- grated Prolog engine, and can be extended with further semantic analyses, e.g., based on abstract interpretation.