Average Vibrational Potentials of Oscillators in Condensed-matter Environments using Hadoop
Date Issued
2014
Author(s)
Abstract
In physical sciences, when condensed matter systems (e.g. solids or liquids) are modeled with an explicit inclusion of dynamical effects, often the following computational problem arises. A given property of an embedded atomic/molecular system within condensed phase should be computed either at different possible structural arrangements and further average over configurations, or alternatively, it is possible to generate an averaged configuration of the dynamical surrounding that
the system experiences and further compute the property of interest at that configuration. The problem of solving the average vibrational potentials of large number of oscillators in various condensed-matter environments (sampled from a statistical physics simulation) can be placed in the category of problems with large data sets. In this paper, a distributed and parallel processing of the large data sets needed for the generation of the averaged vibrational potential is efficiently performed by using the MapReduce programming model and Hadoop software library. Some of the reasons for choosing the Hadoop software library are: It is able to work on data pieces in parallel; The computing solutions enabled by Hadoop are scalable and flexible; The distributed file system enables rapid data transfer among nodes; Hadoop is fault-tolerant which means that if a node fails
the job is redirected to another node. The main goal of this paper is to perform an efficient processing of the large data sets used in the scientific applications.
the system experiences and further compute the property of interest at that configuration. The problem of solving the average vibrational potentials of large number of oscillators in various condensed-matter environments (sampled from a statistical physics simulation) can be placed in the category of problems with large data sets. In this paper, a distributed and parallel processing of the large data sets needed for the generation of the averaged vibrational potential is efficiently performed by using the MapReduce programming model and Hadoop software library. Some of the reasons for choosing the Hadoop software library are: It is able to work on data pieces in parallel; The computing solutions enabled by Hadoop are scalable and flexible; The distributed file system enables rapid data transfer among nodes; Hadoop is fault-tolerant which means that if a node fails
the job is redirected to another node. The main goal of this paper is to perform an efficient processing of the large data sets used in the scientific applications.
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