Application resource requirement estimation in a parallel-pipeline model of execution on a computational grid

We propose a massively parallel framework termed a parallel-pipeline model of execution that can be employed on a computational grid. We show that speedups which are near linear in the number of processors are achievable for applications involving reduction operations based on a novel, parallel-pipeline model of execution. As a computational grids become viable alternative platforms for solving large computational problems, the research community acknowledges that the grid environment can be used effectively when adaptive resource management is employed. This requires the ability to estimate the resource requirements of applications before scheduling decisions are made. We propose a resource estimation model for applications that can be executed in the parallel-pipeline model of execution. We develop a performance model that predicts the resource utilization (i.e., computation and communication complexity) for applications executing under the parallel-pipeline model on a homogeneous computational grid. This performance prediction model can provide information to heterogeneous grid schedulers that are capable of managing multiple homogeneous grids.