We propose a massively parallel framework termed a parallel-pipeline model of execution that can be employed on a homogeneous computational cluster. We show that speedups near-linear in the number of processors are achievable for applications involving reduction operations based on a novel, parallel-pipeline model of execution. As computational clusters become viable alternative platforms for solving large computational problems, the research community acknowledges that the cluster 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 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 cluster. This performance prediction model can provide information to a cluster scheduler.

One of the primary tasks in mining distributed textual data is feature extraction. The widespread digitization of information has created a wealth of data that requires novel approaches to feature extraction in a distributed environment. We propose a massively parallel model for feature extraction that employs unused cycles on networks of PCs/workstations in a highly distributed environment. We have developed an analytical model of the time and communication complexity of the feature extraction process in this environment based on feature extraction algorithms developed in our textual data mining research with HDDI/sup TM/ (Hierarchical Distributed Dynamic Indexing). We show that speedups linear in the number of processors are achievable for applications involving reduction operations based on a novel, parallel pipelined model of execution. We are in the process of validating our analytical model with empirical observations based on the extraction of features from a large number of pages on the World Wide Web.