Heuristic Experiments of Threading and Equal Load Partitioning For Hierarchical Heterogeneous Cluster

Abstract

Presently, the issue of processing large data on a timely manner poses as a challenge to many ICT researchers. Most commodity computers are interconnected in a network forming a cluster computing resource simulating a super computer. This paper explores heuristically the performance of homogeneous, heterogeneous and multi-core clusters. This work consists of five experiments: Equal task partitioning according to the number of nodes in homogeneous cluster, number of nodes in heterogeneous cluster, number of nodes in heterogeneous cluster with multithreading, number of cores in heterogeneous cluster and number of cores in heterogeneous cluster with multithreading. The task is Sobel edge detection method tested with an array of images. The images are processed in three different sizes; 1K × 1K, 2K × 2K and 3K × 3K. The performance evaluations are based on processing speed. The results yield promising impact of equal partitioning and threading in parallel processing hierarchical heterogeneous cluster.