L3C Model of High-Performance Computing Cluster for Scientific Applications

Abstract

High-performance computing clusters (HPCCs) are widely used for various scientific applications. In a typical scientific research environment, software applications need large but varying number of processing elements and processor cores. To maximize throughput of a computing cluster and optimum utilization of resources, one new model has been proposed. The proposed model visualizes the computing cluster as loosely coupled cluster of clusters (L3C). Execution time for scientific applications also varies in terms of lapsed time for execution and CPU time utilized. The process scheduling algorithm maintains a list of applications to be executed along with respective number of node/core required. Using the L3C model and scheduling algorithm, multiple applications are scheduled on the computing cluster for concurrent execution. Basis for proposing L3C model along with its details is discussed in the paper. Experimental results of performance evaluation of HPC clusters were published earlier by the authors and are referred at respective places. L3C model has certain inherent advantages which are also discussed in the paper.