Performance of Sequential SLG Evaluation

SLG is a table-oriented resolution method that is gaining increasing attention due to its ability to combine the deductive database and logic programming paradigms. As an example of its applicability to deductive databases, SLG is asymptotically equivalent to magic template evaluations over the class of deenite programs. In terms of the logic programming (and non-monotonic) paradigms, SLG computes the well-founded model for non-stratiied programs, and has been extended to compute 3-valued and stable model semantics. SLG resembles SLD in that it admits a tuple-at-a-time resolution method, so it can make use of many of the techniques developed in implementing SLD over the past decade. Indeed, a program can contain any mixture of SLG and SLD evaluated predicates. As a result SLG forms a natural basis for extending Prolog systems. This paper presents performance analysis and comparisons of the SLG-WAM, an abstract machine-oriented implementation of SLG. Firstly, the results indicate that when executing SLD, the overhead of the SLG-WAM compared to a similar WAM implementation is minimal, and usually less than 10%. Furthermore, results are presented to indicate tradeoos between SLD and SLG evaluation for a variety of programs. Surprisingly, SLG derivation may be expected to be competitive with SLD derivation on numerous datalog programs, even when SLD terminates and contains no redundant subcomputations. Finally, performance comparisons indicate that the SLG-WAM is about an order of magnitude faster than current deductive database systems even for datalog queries. These results call into question traditional arguments about top-down versus bottom-up evaluation ee-ciency, and may provide information of use in designing and implementing the next generation of logic programming and deductive database systems.