Parallel logic programming systems

Projet CHLOEParallelizing logic programming has attracted much interest in the research community, because of the intrinsic or and and parallelisms of logic programs. One research stream aims at transparent exploitation of parallelism in existing logic programming languages such as Prolog while the family of concurrent logic languages develops constructs allowing programmers to express the concurrency, that is the communication and synchronization between parallel process, inside their algorithms. This paper mainly concentrates on transparent exploitation of parallelism and surveys the most mature solutions to the problems to be solved in order to obtain efficient implementations. These solutions have been implemented and the most efficient parallel logic programming systems reach effective speedups over state-of-the-art sequential Prolog implementations. The paper also addresses current and prospective research issues aiming to extend the applicability and the efficiency of existing systems,such as models merging the transparent parallelism and the concurrent logic languages approaches, combination of constraint logic programming with parallelism and use of highly parallel architectures

Logic programming in the context of multiparadigm programming: the Oz experience

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This article has two goals: to give a tutorial of logic programming in Oz and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We conclude with lessons learned from this work, a brief history of Oz, and many entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic Programming

Coordination using a Single-Writer Multiple-Reader Concurrent Logic Language

The principle behind concurrent logic programming is a set of processes which co-operate in monotonically constraining a global set of variables to particular values. Each process will have access to only some of the variables, and a process may bind a variable to a tuple containing further variables which may be bound later by other processes. This is a suitable model for a coordination language. In this paper we describe a type system which ensures the co-operation principle is never breached, and which makes clear through syntax the pattern of data flow in a concurrent logic program. This overcomes problems previously associated with the practical use of concurrent logic languages

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 90's cannot enjoy an increased level of autonomy without the efficient use of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real time demands are met for large expert systems. Speed-up via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial labs in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems was surveyed. The survey is divided into three major sections: (1) multiprocessors for parallel expert systems; (2) parallel languages for symbolic computations; and (3) measurements of parallelism of expert system. Results to date indicate that the parallelism achieved for these systems is small. In order to obtain greater speed-ups, data parallelism and application parallelism must be exploited