GoMapReduce Parallel Computing Model Implementation on a Cluster of Plan9 Virtual Machines

Abstract

In this paper, we present results of a computational evaluation of goMapReduce parallel programming model approach for solving distributed data processing problems. In some applications, particularly data center problems, including text processing the programming models can aggregate significant number of parallel processes. We first discuss the implementation of these approaches using both Linux and Plan9 operating system and conduct a comparative scalability study of the both. From these results, we empirically show that, in practical implementation and evaluation of a goMapReduce model, the degree of OS's support for distributed processing encountered in solving the resulting word counting problem is crucial. We conclude that the goMapReduce approach under Plan9 may be useful in developing a heuristic approach for the data center problems.

Energy-efficient heterogeneous multiprocessor hardware and software complex

“…-программное обеспечение, поддерживающее ускоренное программирование гетеро-генных аппаратных средств, графические, массивно-параллельные и распределенные вычис-ления и алгоритмы [11].…”

Mapreduce-Based Face Detection in Images

“…MapReduce, a part of Hadoop, is especially interesting as a large amount of problems are easily expressible as Hadoop-based MapReduce computations [1]. As a result, many researches are developing Hadoop-based MapReduce algorithms for sophisticated data analysis like is presented in [2], [3], [4], [5]. Current computer vision applications are not able to use big volumes of data because of the lack of resources for analyzing and storing large amount of data.…”