Design and Implementation of a MapReduce Based Framework for Determinant Computation

Since Google implemented it as their data processing infrastructure, Map Reduce has been widely testified and accepted both in academic and industry areas. Many applications such as graph processing, data mining, machine learning, XML processing used it to get better processing performance in the scalable environment. With the wide spread of Map Reduce, the research on the implementation of the traditional applications in it is meaningful. In this paper, a Map Reduce implementation for the determinant computation is made which supports the repeated and automatic turning of the map and reduce functions as a pipeline. It is suitable for the common calculation for determinants on the basis of an improved Map Reduce on scalable clusters.