scholarly journals Middleware infrastructure for parallel and distributed programming models in heterogeneous systems

2003 ◽  
Vol 14 (11) ◽  
pp. 1100-1111 ◽  
Author(s):  
J. Al-Jaroodi ◽  
N. Mohamed ◽  
Hong Jiang ◽  
D. Swanson
Keyword(s):  
Heterogeneous Systems ◽  
Programming Models ◽  
Distributed Programming

Mixed Parallel Programming Models Using Parallel Tasks

2010 ◽  
pp. 246-275 ◽  
Author(s):  
Joerg Duemmler ◽  
Thomas Rauber ◽  
Gudula Ruenger
Keyword(s):  
Parallel Programming ◽  
Heterogeneous Systems ◽  
Parallel Systems ◽  
Programming Models ◽  
Medium Size ◽  
Data Parallelism ◽  
Communication Structure ◽  
Parallel Tasks ◽  
Parallel Programming Models ◽  
The Future

Parallel programming models using parallel tasks have shown to be successful for increasing scalability on medium-size homogeneous parallel systems. Several investigations have shown that these programming models can be extended to hierarchical and heterogeneous systems which will dominate in the future. In this chapter, the authors discuss parallel programming models with parallel tasks and describe these programming models in the context of other approaches for mixed task and data parallelism. They discuss compiler-based as well as library-based approaches for task programming and present extensions to the model which allow a flexible combination of parallel tasks and an optimization of the resulting communication structure.

Distributed Interoperability in Heterogeneous Cloud Systems

2015 ◽  
pp. 1-40 ◽  
Author(s):  
José C. Delgado
Keyword(s):  
Programming Language ◽  
Binary Data ◽  
Heterogeneous Systems ◽  
The Other ◽  
Full Duplex ◽  
Distributed Programming ◽  
Cloud Systems ◽  
Alternative Technology ◽  
Service Oriented ◽  
Heterogeneous Cloud

Cloud platforms constitute distributed and heterogeneous systems. Interacting applications, possibly in different clouds, face relevant interoperability challenges. This chapter details the interoperability problem and presents an interoperability framework, which provides a systematization of aspects such as coupling, compatibility, and the various levels at which interoperability must occur. After discussing the main limitations of current interoperability technologies, such as Web Services and RESTful applications, the chapter proposes an alternative technology. This entails a new distributed programming language, capable of describing both data and code in a platform-agnostic fashion. The underlying model is based on structured resources, each offering its own service. Service-oriented interfaces can be combined with the structured resources and hypermedia that characterize RESTful applications, instead of having to choose one style or the other. Coupling is reduced by checking interoperability structurally, based on the concepts of compliance and conformance. There is native support for binary data and full-duplex protocols.

An Analysis of Distributed Programming Models and Frameworks for Large-scale Graph Processing

2020 ◽  
pp. 1-9 ◽  
Author(s):  
Alejandro Corbellini ◽  
Daniela Godoy ◽  
Cristian Mateos ◽  
Silvia Schiaffino ◽  
Alejandro Zunino
Keyword(s):  
Large Scale ◽  
Programming Models ◽  
Distributed Programming ◽  
Graph Processing

scholarly journals The MapReduce Model on Cascading Platform for Frequent Itemset Mining

2018 ◽  
Vol 12 (2) ◽  
pp. 149
Author(s):  
Nur Rokhman ◽  
Amelia Nursanti
Keyword(s):  
Large Scale ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Programming Models ◽  
Distributed Programming ◽  
Itemset Mining ◽  
Large Scale Data ◽  
Mapreduce Model ◽  
Large Scale Data Processing ◽  
Scale Data

The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models. The implementation of parallel programming faces many difficulties. The Cascading gives easy scheme of Hadoop system which implements MapReduce model.Frequent itemsets are most often appear objects in a dataset. The Frequent Itemset Mining (FIM) requires complex computation. FIM is a complicated problem when implemented on large-scale data. This paper discusses the implementation of MapReduce model on Cascading for FIM. The experiment uses the Amazon dataset product co-purchasing network metadata.The experiment shows the fact that the simple mechanism of Cascading can be used to solve FIM problem. It gives time complexity O(n), more efficient than the nonparallel which has complexity O(n2/m).

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