Abstract State Machines 1988-1998: Commented ASM Bibliography

An annotated bibliography of papers which deal with or use Abstract State Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm

Logic programming for real-time control of telecommunication switching systems

AbstractAn experiment using logic programming in the specification and implementation of a telecommunication switching system is reported, and one of the main modules in the system, a telephone-line controller, is described in detail as an illustrative example. The system is described in terms of transition relations in a labeled transition system. The programming language used is a variant of the parallel logic language PARLOG augmented with annotations to express timing constraints. The operational model of PARLOG is modified to handle time by allowing each goal-reduction process in a query to maintain its own logical clock, which can be read and set by the goal-reduction process itself. A metainterpreter is given to describe the operational behavior and an implementation scheme for the language

Programming Languages for Distributed Computing Systems

When distributed systems first appeared, they were programmed in traditional sequential languages, usually with the addition of a few library procedures for sending and receiving messages. As distributed applications became more commonplace and more sophisticated, this ad hoc approach became less satisfactory. Researchers all over the world began designing new programming languages specifically for implementing distributed applications. These languages and their history, their underlying principles, their design, and their use are the subject of this paper. We begin by giving our view of what a distributed system is, illustrating with examples to avoid confusion on this important and controversial point. We then describe the three main characteristics that distinguish distributed programming languages from traditional sequential languages, namely, how they deal with parallelism, communication, and partial failures. Finally, we discuss 15 representative distributed languages to give the flavor of each. These examples include languages based on message passing, rendezvous, remote procedure call, objects, and atomic transactions, as well as functional languages, logic languages, and distributed data structure languages. The paper concludes with a comprehensive bibliography listing over 200 papers on nearly 100 distributed programming languages

Lambda Calculus in Core Aldwych

Core Aldwych is a simple model for concurrent computation, involving the concept of agents which communicate through shared variables. Each variable will have exactly one agent that can write to it, and its value can never be changed once written, but a value can contain further variables which are written to later. A key aspect is that the reader of a value may become the writer of variables in it. In this paper we show how this model can be used to encode lambda calculus. Individual function applications can be explicitly encoded as lazy or not, as required. We then show how this encoding can be extended to cover functions which manipulate mutable variables, but with the underlying Core Aldwych implementation still using only immutable variables. The ordering of function applications then becomes an issue, with Core Aldwych able to model either the enforcement of an ordering or the retention of indeterminate ordering, which allows parallel execution

Specifying and reasoning about concurrent systems in logic

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Design, application and implementation of a paralled logic programming language

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Probabilistic Graphical Models on Multi-Core CPUs using Java 8

In this paper, we discuss software design issues related to the development of parallel computational intelligence algorithms on multi-core CPUs, using the new Java 8 functional programming features. In particular, we focus on probabilistic graphical models (PGMs) and present the parallelisation of a collection of algorithms that deal with inference and learning of PGMs from data. Namely, maximum likelihood estimation, importance sampling, and greedy search for solving combinatorial optimisation problems. Through these concrete examples, we tackle the problem of defining efficient data structures for PGMs and parallel processing of same-size batches of data sets using Java 8 features. We also provide straightforward techniques to code parallel algorithms that seamlessly exploit multi-core processors. The experimental analysis, carried out using our open source AMIDST (Analysis of MassIve Data STreams) Java toolbox, shows the merits of the proposed solutions.Comment: Pre-print version of the paper presented in the special issue on Computational Intelligence Software at IEEE Computational Intelligence Magazine journa