Efficient manipulation of large datasets on heterogeneous storage systems

We are concerned with the efficient use of a collection of disk-based storage systems and computing platforms in a heterogeneous setting for retrieving and processing large scientific datasets. We demonstrate, in the context of a data-intensive visualization application, how heterogeneity affects performance and show a set of optimization techniques that can be used to improve performance in a component-based framework. In particular, we examine the application of parallelism via transparent copies of application components in the pipelined processing of data.