Structural operational semantics for Kernel Andorra Prolog

Kernel Andorra Prolog is a framework for nondeterministic concurrent constraint logic programming languages. Many languages, such as Prolog, GHC, Parlog, and Atomic Herbrand, can be seen as instances of this framework, by adding specific constraint systems and constraint operations, and optionally by imposing further restrictions on the language and the control of the computation model. We systematically revisit the description in Haridi and Jarison [HJ90], adding the formal machinery which is necessary in order to completely formalize the control of the computation model. To this we add a formal description of the transformational semantics of Kernel Andorra Prolog. The semantics of Kernel Andorra Prolog is a set of or-trees which also captures infinite computations

Or-Parallel Prolog Execution on Clusters of Multicores

Logic Programming languages, such as Prolog, provide an excellent framework for the parallel execution of logic programs. In particular, the inherent non-determinism in the way logic programs are structured makes Prolog very attractive for the exploitation of implicit parallelism. One of the most noticeable sources of implicit parallelism in Prolog programs is or-parallelism. Or-parallelism arises from the simultaneous evaluation of a subgoal call against the clauses that match that call. Arguably, the most successful model for or-parallelism is environment copying, that has been efficiently used in the implementation of or-parallel Prolog systems both on shared memory and distributed memory architectures. Nowadays, multicores and clusters of multicores are becoming the norm and, although, many 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 with distributed memory architectures. Motivated by our past experience, in designing and developing parallel Prolog systems based on environment copying, we propose a novel computational model to efficiently exploit implicit parallelism from large scale real-world applications specialized for the novel architectures based on clusters of multicores

Distributed multi-threading in GNU prolog

Embora a computação paralela já tenha sido alvo de inúmeros estudos, o processo de a tornar acessível as massas ainda mal começou. Através da combinação com o Prolog de um ambiente de programação distribuída e multithreaded, como o PM2, torna-se possível ter computações paralelas e concorrentes usando programação em logica. Com este objetivo foi desenvolvido o PM2-Prolog, um interface Prolog para o sistema PM2. Tal sistema permite correr aplicações Prolog multithreaded em múltiplas instâncias do GNU Prolog num ambiente distribuído, tirando, assim, partido dos recursos disponíveis nos computadores ligados numa rede. Em problemas computacionalmente pesados, onde o tempo de execução é crucial, existe particular vantagem em usar este sistema. A API do sistema oferece primitivas para gestão de threads e para comunicação explícita entre threads. Testes preliminares mostram um ganho de desempenho quase linear, em comparação com uma versão sequencial. /ABSTRACT - Although parallel computing has been widely researched, the process of bringing concurrency and parallel programming to the mainstream has just begun. Combining a distributed multi-threading environment like PM2 with Prolog, opens the way to exploit concurrency and parallel computing using logic programming. To achieve such a purpose, we developed PM2-Prolog, a Prolog interface to the PM2 system. It allows multithreaded Prolog applications to run in multiple GNU Prolog engines in a distributed environment, thus taking advantage of the resources available on a computer network. This is especially useful for computationally intensive problems, where performance is an important factor. The system API offers thread management primitives, as well as explicit communication between threads. Preliminary test results show an almost linear speedup, when compared to a sequential version

The Oz programming model

The Oz Programming Model (OPM) is a concurrent programming model subsuming higher-order functional and object-oriented programming as facets of a general model. This is particularly interesting for concurrent object-oriented programming, for which no comprehensive formal model existed until now. The model can be extended so that it can express encapsulated problem solvers generalizing the problem solving capabilities of constraint logic programming. OPM has been developed together with a concomitant programming language Oz, which is designed for applications that require complex symbolic computations, organization into multiple agents, and soft real-time control. An efficient, robust, and interactive implementation of Oz is freely available

An abstract model for parallel execution of prolog

Logic programming has been used in a broad range of fields, from artifficial intelligence applications to general purpose applications, with great success. Through its declarative semantics, by making use of logical conjunctions and disjunctions, logic programming languages present two types of implicit parallelism: and-parallelism and or-parallelism. This thesis focuses mainly in Prolog as a logic programming language, bringing out an abstract model for parallel execution of Prolog programs, leveraging the Extended Andorra Model (EAM) proposed by David H.D. Warren, which exploits the implicit parallelism in the programming language. A meta-compiler implementation for an intermediate language for the proposed model is also presented. This work also presents a survey on the state of the art relating to implemented Prolog compilers, either sequential or parallel, along with a walk-through of the current parallel programming frameworks. The main used model for Prolog compiler implementation, the Warren Abstract Machine (WAM) is also analyzed, as well as the WAM’s successor for supporting parallelism, the EAM; Sumário: Um Modelo Abstracto para Execução Paralela de Prolog A programação em lógica tem sido utilizada em diversas áreas, desde aplicações de inteligência artificial até aplicações de uso genérico, com grande sucesso. Pela sua semântica declarativa, fazendo uso de conjunções e disjunções lógicas, as linguagens de programação em lógica possuem dois tipos de paralelismo implícito: ou-paralelismo e e-paralelismo. Esta tese foca-se em particular no Prolog como linguagem de programação em lógica, apresentando um modelo abstracto para a execução paralela de programas em Prolog, partindo do Extended Andorra Model (EAM) proposto por David H.D. Warren, que tira partido do paralelismo implícito na linguagem. É apresentada uma implementação de um meta-compilador para uma linguagem intermédia para o modelo proposto. É feita uma revisão sobre o estado da arte em termos de implementações sequenciais e paralelas de compiladores de Prolog, em conjunto com uma visita pelas linguagens para implementação de sistemas paralelos. É feita uma análise ao modelo principal para implementação de compiladores de Prolog, a Warren Abstract Machine (WAM) e da sua evolução para suportar paralelismo, a EAM

Effectiveness of combined sharing and freeness analysis using abstract interpretation

This paper presents improved unification algorithms, an implementation, and an analysis of the effectiveness of an abstract interpreter based on the sharing + freeness domain presented in a previous paper, which was designed to accurately and concisely represent combined freeness and sharing information for program variables. We first briefly review this domain and the unification algorithms previously proposed. We then improve these algorithms and correct them to deal with some cases which were not well analyzed previously, illustrating the improvement with an example. We then present the implementation of the improved algorithm and evaluate its performance by comparing the effectiveness of the information inferred to that of other interpreters available to us for an application (program parallelization) that is common to all these interpreters. All these systems have been embedded in a real parallelizing compiler. Effectiveness of the analysis is measured in terms of actual final performance of the system: i.e. in terms of the actual speedups obtained. The results show good performance for the combined domain in that it improves the accuracy of both types of information and also in that the analyzer using the combined domain is more effective in the application than any of the other analyzers it is compared to