Linda implementations in Java for concurrent systems

Wells, George; Chalmers, Alan; Clayton, Peter

doi:10.1002/cpe.794

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…Our work follows the lines of [34] but we have chosen more recent implementations and conducted more extensive experiments. On purpose, we ignored implementations of systems that have been directly inspired by those considered in the paper.…”

Section: Discussionmentioning

confidence: 99%

Tuple Spaces Implementations and Their Efficiency

Buravlev

Nicola

Mezzina

2016

Lecture Notes in Computer Science

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SUMMARYAmong the paradigms for parallel and distributed computing, the one popularized with Linda, and based on tuple spaces, is one of the least used, despite the fact of being intuitive, easy to understand and to use. A tuple space is a repository, where processes can add, withdraw or read tuples by means of atomic operations. Tuples may contain different values, and processes can inspect their content via pattern matching. The lack of a reference implementation for this paradigm has prevented its widespread. In this paper, first we perform an extensive analysis of a number of actual implementations of the tuple space paradigm and summarise their main features. Then, we select four such implementations and compare their performances on four different case studies that aim at stressing different aspects of computing such as communication, data manipulation, and cpu usage. After reasoning on strengths and weaknesses of the four implementations, we conclude with some recommendations for future work towards building an effective implementation of the tuple space paradigm.

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Section: Discussionmentioning

confidence: 99%

Tuple Spaces Implementations and Their Efficiency

Buravlev

Nicola

Mezzina

2016

Lecture Notes in Computer Science

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“…An early approach that treats coordination and computation as orthogonal concerns is Linda [11]. Implementations of the Linda model can be found for a variety of programming languages [12], [13].…”

Section: Related Workmentioning

confidence: 99%

Distributed S-Net: Cluster and Grid Computing without the Hassle

Grelck

Julku

Penczek³

2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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S-NET is a declarative coordination language and component technology primarily aimed at modern multicore/many-core chip architectures. It builds on the concept of stream processing to structure dynamically evolving networks of communicating asynchronous components. Components themselves are implemented using a conventional language suitable for the application domain.We present the design and implementation of Distributed S-NET, a conservative extension of S-NET geared towards distributed memory architectures ranging from many-core chip architectures with hierarchical memory organizations to more traditional clusters of workstations, supercomputers and grids.Three case studies illustrate how to use Distributed S-NET to implement different models of parallel execution. Runtimes obtained on a workstation cluster demonstrate how Distributed S-NET allows programmers with little or no background in parallel programming to make effective use of distributed memory architectures with minimal programming effort.

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“…It was first proposed by David Gelernter at Yale University in the mid-1980's [9] and in recent years there has been a resurgence in interest, particularly with regard to Java implementations of Linda [28,29]. On distributed memory systems, such as networks of workstations, the tuple space is usually distributed among the processing nodes.…”

Section: Linda Basicsmentioning

confidence: 99%

On the concurrent execution of model transformations with Linda

Burgueño

Troya

Wimmer

et al. 2013

Proceedings of the Workshop on Scalability in Model Driven Engineering

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Nowadays there exists a wide variety of model transformation languages. However, all of them present limitations, mainly performance issues, when the complexity and size of model transformations and models grow. The problems arise due to the in-memory allocation of large models as well as the time taken by the execution engines for producing the output models. This restricts the benefits of using model transformations in different application fields of model engineering where the complexity of the transformation tasks exceeds the capabilities of sequential execution engines. In this paper we tackle these limitations by introducing concurrency for model transformations to effectively improve the execution performance. Instead of reinventing the wheel, we base our approach on Linda, a mature coordination language for parallel processes. We explore how model transformations fit into Linda and show a set of basic mechanisms to enable concurrent model transformations. Initial results of applying our approach show a great potential of using Linda to improve the execution performance with respect to existing approaches.

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Linda implementations in Java for concurrent systems

Cited by 18 publications

References 15 publications

Tuple Spaces Implementations and Their Efficiency

Tuple Spaces Implementations and Their Efficiency

Distributed S-Net: Cluster and Grid Computing without the Hassle

On the concurrent execution of model transformations with Linda

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