Comparing MapReduce and pipeline implementations for counting triangles
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/100579
Tipus de documentText en actes de congrés
Data publicació2016
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement 3.0 Espanya
ProjecteMODELOS Y METODOS COMPUTACIONALES PARA DATOS MASIVOS ESTRUCTURADOS (MINECO-TIN2013-46181-C2-1-R)
Abstract
A generalized method to define the Divide & Conquer paradigm in order to have processors acting on its own data and scheduled in a
parallel fashion. MapReduce is a programming model that follows this paradigm, and allows for the definition of efficient solutions by both decomposing a problem into steps on subsets of the input data
and combining the results of each step to produce final results. Albeit used for the implementation of a wide variety of computational problems, MapReduce performance can be negatively affected
whenever the replication factor grows or the size of the input is larger than the resources available at each processor. In this paper we show an alternative approach to implement the Divide & Conquer
paradigm, named pipeline. The main features of pipeline are illustrated on a parallel implementation of the well-known problem of counting triangles in a graph. This problem is especially interesting either when the input graph does not fit in memory or is dynamically generated. To evaluate the properties of pipeline, a dynamic pipeline of processes and an ad-hoc version of MapReduce are implemented in the language Go, exploiting its ability to deal with channels and spawned processes.
An empirical evaluation is conducted on graphs of different sizes and densities. Observed results suggest that pipeline allows for the implementation of an efficient solution of the problem of counting
triangles in a graph, particularly, in dense and large graphs, drastically reducing the execution time with respect to the MapReduce implementation.
CitacióEdelmira Pasarella, Maria-Esther Vidal, Cristina Zoltan. Comparing MapReduce and pipeline implementations for counting triangles. A: Jornadas sobre Programación y Lenguajes. "Actas de las XVI Jornadas de Programación y Lenguajes (PROLE 2016): Salamanca, septiembre de 2016". Salamanca: 2016, p. 178-187.
Versió de l'editorhttp://biblioteca.sistedes.es/biblioteca/conferencias/prole/prole-2016/
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