Abstract
Tabling is a technique of resolution that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant sub-computations. We can distinguish two main categories of tabling mechanisms: suspension-based tabling and linear tabling. In suspension-based tabling, a tabled evaluation can be seen as a sequence of sub-computations that suspend and later resume. Linear tabling mechanisms maintain a single execution tree where tabled subgoals always extend the current computation without requiring suspension and resumption of sub-computations. In this work, we present a new and efficient implementation of linear tabling, but for that we have extended an already existent suspension-based implementation, the YapTab engine. Our design is based on dynamic reordering of alternatives but it innovates by considering a strategy that schedules the re-evaluation of tabled calls in a similar manner to the suspension-based strategies of YapTab. Our implementation also shares the underlying execution environment and most of the data structures used to implement tabling in YapTab. We thus argue that all these common features allows us to make a first and fair comparison between suspension-based and linear tabling and, therefore, better understand the advantages and weaknesses of each.

Abstract
The execution model on which most tabling engines are based allocates a choice point whenever a new tabled subgoal is called. This happens even when the call is deterministic. however, sortie of the information from the choice point; is never used when evaluating deterministic tabled calls with batched scheduling. Moreover, when a deterministic answer is found for a. deterministic tabled call, the call can be completed early and the corresponding choice point can be removed. Thus, if applying batched scheduling to a long deterministic computation the system may end up consuming memory and evaluating calls unnecessarily. In this paper, we propose a solution that, tries to reduce this memory and execution overhead to a minimum. Our experimental results show that;, for deterministic tabled calls and tabled answers with batched scheduling, it; is possible not. only to reduce the memory usage overhead, but also the running time of tire execution.

Abstract
One of the main advantages of Prolog is its potential for the implicit exploitation of parallelism and, as a high-level language, Prolog is also often used as a means to explicitly control concurrent tasks. Tabling is a powerful implementation technique that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant subcomputations. Given these advantages, the question that arises is if tabling has also the potential for the exploitation of concurrency/parallelism. On one hand, tabling still exploits a search space as traditional Prolog but, on the other hand, the concurrent model of tabling is necessarily far more complex, since it also introduces concurrency on the access to the tables. In this paper, we summarize Yap's main contributions to concurrent tabled evaluation and we describe the design and implementation challenges of several alternative table space designs for implicit and explicit concurrent tabled evaluation that represent different tradeoffs between concurrency and memory usage. We also motivate for the advantages of using fixed-size and lock freedata structures, elaborate on the key role that the engine's memory allocator plays on such environments, and discuss how Yap's mode-directed tabling support can be extended to concurrent evaluation. Finally, we present our future perspectives toward an efficient and novel concurrent framework which integrates both implicit and explicit concurrent tabled evaluation in a single Prolog engine.