Abstract
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.

Abstract
Declarative programming has been hailed as a promising approach to parallel programming since it makes it easier to reason about programs while hiding the implementation details of parallelism from the programmer. However, its advantage is also its disadvantage as it leaves the programmer with no straightforward way to optimize programs for performance. In this paper, we introduce Coordinated Linear Meld (CLM), a concurrent forward-chaining linear logic programming language, with a declarative way to coordinate the execution of parallel programs allowing the programmer to specify arbitrary scheduling and data partitioning policies. Our approach allows the programmer to write graph-based declarative programs and then optionally to use coordination to fine-tune parallel performance. In this paper we specify the set of coordination facts, discuss their implementation in a parallel virtual machine, and show-through example-how they can be used to optimize parallel execution. We compare the performance of CLM programs against the original uncoordinated Linear Meld and several other frameworks.

Abstract
Tabling is an implementation technique that improves the declarativeness and expressiveness of Prolog systems in dealing with recursion and redundant sub-computations. A critical component in the design of a concurrent tabling system is the implementation of the table space. One of the most successful proposals for representing tables is based on a two-level trie data structure, where one trie level stores the tabled subgoal calls and the other stores the computed answers. In previous work, we have presented a sophisticated lock-free design where both levels of the tries where shared among threads in a concurrent environment. To implement lock-freedom we used the CAS atomic instruction that nowadays is widely found on many common architectures. CAS reduces the granularity of the synchronization when threads access concurrent areas, but still suffers from problems such as false sharing or cache memory effects. In this work, we present a simpler and efficient lock-free design based on hash tries that minimizes these problems by dispersing the concurrent areas as much as possible. Experimental results in the Yap Prolog system show that our new lock-free design can effectively reduce the execution time and scales better than previous designs.

Abstract
Probabilistic Inductive Logic Programming (PILP) systems extend ILP by allowing the world to be represented using probabilistic facts and rules, and by learning probabilistic theories that can be used to make predictions. However, such systems can be inefficient both due to the large search space inherited from the ILP algorithm and to the probabilistic evaluation needed whenever a new candidate theory is generated. To address the latter issue, this work introduces probability estimators aimed at improving the efficiency of PILP systems. An estimator can avoid the computational cost of probabilistic theory evaluation by providing an estimate of the value of the combination of two subtheories. Experiments are performed on three real-world datasets of different areas (biology, medical and web-based) and show that, by reducing the number of theories to be evaluated, the estimators can significantly shorten the execution time without losing probabilistic accuracy. © Springer International Publishing AG 2017.

Abstract

Abstract
Markov Logic is an expressive and widely used knowledge representation formalism that combines logic and probabilities, providing a powerful framework for inference and learning tasks. Most Markov Logic implementations perform inference by transforming the logic representation into a set of weighted propositional formulae that encode a Markov network, the ground Markov network. Probabilistic inference is then performed over the grounded network. Constructing, simplifying, and evaluating the network are the main steps of the inference phase. As the size of a Markov network can grow rather quickly, Markov Logic Network (MLN) inference can become very expensive, motivating a rich vein of research on the optimization of MLN performance. We claim that parallelism can have a large role on this task. Namely, we demonstrate that widely available Graphics Processing Units (GPUs) can be used to improve the performance of a state-of-the-art MLN system, Tuffy, with minimal changes. Indeed, comparing the performance of our GPU-based system, TuGPU, to that of the Alchemy, Tuffy and RockIt systems on three widely used applications shows that TuGPU is up to 15x times faster than the other systems.