Impact Assessment on the Parallel Performance of Node-Core Combinations in a Multicore Cluster Environment: A Case of Study

Abstract

Multicore processors have opened new paths for improving the parallel performance in cluster environments. Nevertheless, the selection of different combinations between the amount of nodes and the number of cores per node implies different results in terms of parallel performance. We performed an impact assessment on the parallel performance of node-core combinations using a parallel approach of a machine learning ensemble algorithm. Our results reveal that two key factors for selecting a suitable node-core combination: the network capabilities and the workload distribution. We observed that the network interconnection limits the amount of nodes that can be efficiently used, due to the extranode communications does not allow to keep scaling as the number of nodes is increased. The best results were obtained by reaching a balance between intra-node and extra-node communications. By the other hand, the parallel performance can be negatively affected when the workload distribution is not homogeneous among nodes.