Philip J. Rhodes

Learn More
We describe two techniques for rendering isosurfaces in multiresolution volume data so that the uncertainty (error) in the data is shown in the visualization. In general the visualization of uncertainty in data is difficult, but the nature of isosurface rendering makes it amenable to an effective solution. In addition to showing the error in the data used(More)
Most caching and prefetching research does not take advantage of prior knowledge of access patterns, or does not adequately address the storage issues inherent with multidimensional scientific data. Armed with an access pattern specified as an iteration over a multidimensional array stored in a disk file, we use prefetching to greatly reduce the number of(More)
Modern dataset sizes present major obstacles to understanding and interpreting the significant underlying phenomena represented in the data. There is a critical need to support scientists in the process of interactive exploration of these very large data sets. Using multiple resolutions of the data set (multiresolution), the scientist can identify(More)
Visualization of multidimensional data presents special challenges for the design of efficient out-of-core data access. Elements that are nearby in the visualization may not be nearby in the underlying data file, which can severely tax the operating system’s disk cache. The Granite Scientific Database System can address these problems because it is aware of(More)
1 Introduction New data gathering and data generation tools have created an explosion in the amount of data available to scientists. The existence of such large amounts of data provides opportunities that have not previously been possible, but the dataset sizes present major obstacles to understanding and interpreting the significant underlying phenomena(More)
Downloading an entire file is not practical for very large n-dimensional (n-d) datasets, especially if the region of interest (ROI) is small. It is therefore important to develop methods to allow researchers to remotely access n-d subsets of large datasets. Since researchers often wish to access a series of subsets, an awareness of the relationship between(More)
Although processing speed, storage capacity and network bandwidth are steadily increasing, network latency remains a bottleneck for scientists accessing large remote data sets. This problem is most acute with n-dimensional data. Grid researchers have only recently begun to develop tools for efficient remote access to n-dimensional data sets. Within the(More)
Disk and network latency must be taken into account when applying parallel computing to large multidimensional datasets because they can hinder performance by reducing the rate at which data can be fed to the compute nodes. Existing methods aggregate some number of data requests from cluster nodes to improve overall performance by reducing the number of(More)