Convex Hull Using K-Means Clustering in Hybrid (MPI/OpenMP) Environment

Parallel computing is the use of multiple compute resources to solve a computational problem. Parallel computers can be roughly classified as Multi-Core and Multi-Processor. In both these classifications, the hardware supports parallelism with computer node having multiple processing elements in a single machine. Parallel programming is the ability of program to run on this infrastructure which is still quite difficult and complex task to achieve. Two of the different approaches used in parallel environment are MPI and Open MP, each one of them have their own merits and demerits. Hybrid model combines both approaches in the pursuit of reducing the weaknesses in individual. In proposed approach k-Means Clustering algorithm used for solving the problem of Convex Hull in parallel environment. In this design, 2D points are grouped into different Cluster and then Convex hull for each of these Clusters are computed. Points defining these Convex hulls are used to construct the final Convex hull. This algorithm is implemented in MPI, OpenMP, and Hybrid mode. The algorithm is tested for number of Clusters with different set of points. The results indicates that the Hybrid approach out performs the MPI and OpenMP approach.