Load balancing on PC clusters with the super-programming model

Recent work in high-performance computing has shifted attention to PC cluster. For PC-clusters, member nodes are independent computers connected by general-purpose networks. The latency of data communications is long and load balancing among the nodes becomes a critical issue. We introduce a new model for program development on PC clusters, namely the super-programming model (SPM) to address this issue. In SPM, PC clusters are modeled as a single virtual machine with PC as their processing units. The workload is modeled as a collection of super-instructions (SI). Each SI can achieve a limited workload. Application programs are coded using SI. SI is dynamically assigned to available PC at run time. For limited workload, no SI overloads any PC. Therefore, dynamic load balancing becomes an easier task. We apply SPM to mining association rules. Our experiments show that under normal conditions the workload is balanced very well. A performance model is also developed to describe the scalable behavior of SPM.