27,888 research outputs found
The CIAO multiparadigm compiler and system: A progress report
Abstract is not available
The CIAO Multi-Dialect Compiler and System: An Experimentation Workbench for Future (C)LP Systems
CIAO is an advanced programming environment supporting Logic and Constraint programming. It offers a simple concurrent kernel on top of which declarative and non-declarative extensions are added via librarles. Librarles are available for supporting the ISOProlog standard, several constraint domains, functional and higher order programming, concurrent and distributed programming, internet programming, and others. The source language allows declaring properties of predicates via assertions, including types and modes. Such properties are checked at compile-time or at run-time. The compiler and system architecture are designed to natively support modular global analysis, with the two objectives of proving properties in assertions and performing program optimizations, including transparently exploiting parallelism in programs. The purpose of this paper is to report on recent progress made in the context of the CIAO system, with special emphasis on the capabilities of the compiler, the techniques used for supporting such capabilities, and the results in the áreas of program analysis and transformation already obtained with the system
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
An Integrated Development Environment for Declarative Multi-Paradigm Programming
In this paper we present CIDER (Curry Integrated Development EnviRonment), an
analysis and programming environment for the declarative multi-paradigm
language Curry. CIDER is a graphical environment to support the development of
Curry programs by providing integrated tools for the analysis and visualization
of programs. CIDER is completely implemented in Curry using libraries for GUI
programming (based on Tcl/Tk) and meta-programming. An important aspect of our
environment is the possible adaptation of the development environment to other
declarative source languages (e.g., Prolog or Haskell) and the extensibility
w.r.t. new analysis methods. To support the latter feature, the lazy evaluation
strategy of the underlying implementation language Curry becomes quite useful.Comment: In A. Kusalik (ed), proceedings of the Eleventh International
Workshop on Logic Programming Environments (WLPE'01), December 1, 2001,
Paphos, Cyprus. cs.PL/011104
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
Analyzing logic programs with dynamic scheduling
Traditional logic programming languages, such as Prolog, use a fixed left-to-right atom scheduling rule. Recent logic programming languages, however, usually provide more flexible scheduling in which computation generally proceeds leftto- right but in which some calis are dynamically
"delayed" until their arguments are sufRciently instantiated
to allow the cali to run efficiently. Such dynamic scheduling has a significant cost. We give a framework for the global analysis of logic programming languages with dynamic scheduling and show that program analysis based on this framework supports optimizations which remove much
of the overhead of dynamic scheduling
A Simulation Tool for tccp Programs
The Timed Concurrent Constraint Language tccp is a declarative synchronous concurrent language, particularly suitable for modelling reactive systems. In tccp, agents communicate and synchronise through a global constraint store. It supports a notion of discrete time that allows all non-blocked agents to proceed with their execution simultaneously.
In this paper, we present a modular architecture for the simulation of tccp programs. The tool comprises three main components. First, a set of basic abstract instructions able to model the tccp agent behaviour, the memory model needed to manage the active agents and the state of the store during the execution. Second, the agent interpreter that executes the instructions of the current agent iteratively and calculates the new agents to be executed at the next time instant. Finally, the constraint solver components which are the modules that deal with constraints.
In this paper, we describe the implementation of these components and present an example of a real system modelled in tccp.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
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