scholarly journals Supporting automatic recovery in offloaded distributed programming models through MPI-3 techniques

2017 ◽  
Author(s):  
Antonio J. Peña ◽  
Vicenç Beltran ◽  
Carsten Clauss ◽  
Thomas Moschny
Keyword(s):  
Programming Models ◽  
Distributed Programming ◽  
Automatic Recovery

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).

scholarly journals The Impact of Java Applications at Microarchitectural Level from Branch Prediction Perspective

2009 ◽  
Vol 4 (1) ◽  
pp. 27 ◽  
Author(s):  
Adrian Florea ◽  
Arpad Gellert ◽  
Lucian N. Vințan ◽  
Marius N. Velțan
Keyword(s):  
Design Patterns ◽  
Prediction Accuracy ◽  
Object Oriented ◽  
Branch Prediction ◽  
Programming Models ◽  
Distributed Programming ◽  
Application Behavior ◽  
Significant Performance ◽  
Application Developers ◽  
The Impact

The portability, the object-oriented and distributed programming models, multithreading support and automatic garbage collection are features that make Java very attractive for application developers. The main goal of this paper consists in pointing out the impact of Java applications at microarchitectural level from two perspectives: unbiased branches and indirect jumps/calls, such branches limiting the ceiling of dynamic branch prediction and causing significant performance degradation. Therefore, accurately predicting this kind of branches remains an open problem. The simulation part of the paper mainly refers to determining the context length influence on the percentage of unbiased branches from Java applications, the prediction accuracy and the usage degree obtained using a Fast Path-Based Perceptron predictor. We realize a comparison with C/C++ application behavior from unbiased branches perspective. We also analyze some Java testing programs, built using design patterns or including inheritance, polymorphism, backtracking and recursivity, in order to determine the features of indirect branches, the arity of each indirect jump and the prediction accuracy using the Target Cache predictor.

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