An Efficient Parallel Computing Method for the Processing of Large Sensed Data

Abstract

In recent years we witness the advent of the Internet of Things and the wide deployment of sensors in many applications for collecting and aggregating data. Efficient techniques are required to analyze these massive data for supporting intelligent decisions making. Partial differential problems which involve large data are the most common in the engineering and scientific research. For simulations of large-scale three-dimensional partial differential equations, the intensive computation ability and large amounts of memory requirements for modeling are the main research problems. To address the two challenges, this paper provided an effective parallel method for partial differential equations. The proposed approach combines the overlapping domain decomposition strategy and the multi-core cluster technology to achieve parallel simulations of partial differential equations, uses the finite difference method to discretize equations and adopts the hybrid MPI/OpenMP programming model to exploit two-level parallelism on a multi-core cluster. The three-dimensional groundwater flow model with the parallel finite difference overlapping domain decomposition strategy was successfully set up and carried out by the parallel MPI/OpenMP implementation on a multi-core cluster with two nodes. The experimental results show that the proposed parallel approach can efficiently simulate partial differential problems with large amounts of data.