The dlvhex system implements the hex-semantics, which integrates answer set programming (ASP) with arbitrary external sources. Since its first release ten years ago, significant advancements were achieved. Most importantly, the exploitation of properties of external sources led to efficiency improvements and flexibility enhancements of the language, and technical improvements on the system side increased user's convenience. In this paper, we present the current status of the system and point out the most important recent enhancements over early versions. While existing literature focuses on theoretical aspects and specific components, a bird's eye view of the overall system is missing. In order to promote the system for real-world applications, we further present applications which were already successfully realized on top of dlvhex.

Recent work has provided delimited control for Prolog to dynamically manipulate the program control-flow, and to implement a wide range of control-flow and dataflow effects on top of. Unfortunately, delimited control is a rather primitive language feature that is not easy to use.

As a remedy, this work introduces algebraic effect handlers for Prolog, as a high-level and structured way of defining new side-effects in a modular fashion. We illustrate the expressive power of the feature and provide an implementation by means of elaboration into the delimited control primitives.

The latter add a non-negligible performance overhead when used extensively. To address this issue, we present an optimised compilation approach that combines partial evaluation with dedicated rewrite rules. The rewrite rules are driven by a lightweight effect inference that analyses what effect operations may be called by a goal. We illustrate the effectiveness of this approach on a range of benchmarks.

Nowadays, clusters of multicores are becoming the norm and, although, many or-parallel Prolog systems have been developed in the past, to the best of our knowledge, none of them was specially designed to explore the combination of shared and distributed memory architectures. In recent work, we have proposed a novel computational model specially designed for such combination which introduces a layered model with two scheduling levels, one for workers sharing memory resources, which we named a team of workers, and another for teams of workers (not sharing memory resources). In this work, we present a first implementation of such model and for that we revive and extend the YapOr system to exploit or-parallelism between teams of workers. We also propose a new set of built-in predicates that constitute the syntax to interact with an or-parallel engine in our platform. Experimental results show that our implementation is able to increase speedups as we increase the number of workers per team, thus taking advantage of the maximum number of cores in a machine, and to increase speedups as we increase the number of teams, thus taking advantage of adding more computer nodes to a cluster.

Pointer analysis is a fundamental static program analysis for computing the set of objects that an expression can refer to. Decades of research has gone into developing methods of varying precision and efficiency for pointer analysis for programs that use different language features, but determining precisely how efficient a particular method is has been a challenge in itself.

For programs that use different language features, we consider methods for pointer analysis using Datalog and extensions to Datalog. When the rules are in Datalog, we present the calculation of precise time complexities from the rules using a new algorithm for decomposing rules for obtaining the best complexities. When extensions such as function symbols and universal quantification are used, we describe algorithms for efficiently implementing the extensions and the complexities of the algorithms.

Tabling is a powerful resolution mechanism for logic programs that captures their least fixed point semantics more faithfully than plain Prolog. In many tabling applications, we are not interested in the set of all answers to a goal, but only require an aggregation of those answers. Several works have studied efficient techniques, such as lattice-based answer subsumption and mode-directed tabling, to do so for various forms of aggregation.

While much attention has been paid to expressivity and efficient implementation of the different approaches, soundness has not been considered. This paper shows that the different implementations indeed fail to produce least fixed points for some programs. As a remedy, we provide a formal framework that generalises the existing approaches and we establish a soundness criterion that explains for which programs the approach is sound.

The runtime system of dynamic languages such as Prolog or Lisp and their derivatives contain a symbol table, in Prolog often called the atom table. A simple dynamically resizing hash-table used to be an adequate way to implement this table. As Prolog becomes fashionable for 24 × 7 server processes we need to deal with atom garbage collection and concurrent access to the atom table. Classical lock-based implementations to ensure consistency of the atom table scale poorly and a stop-the-world approach to implement atom garbage collection quickly becomes a bottle-neck, making Prolog unsuitable for soft real-time applications. In this article we describe a novel implementation for the atom table using lock-free techniques where the atom-table remains accessible even during atom garbage collection. Relying only on CAS (Compare And Swap) and not on external libraries, the implementation is straightforward and portable.