Philip J. Rhodes

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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)
1 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(More)
High performance parallel computing infrastructures, such as computing clusters, have recently become freely available for scientific researchers to solve problems of unprecedented scale through data parallelization. However scientists are not necessarily skilled in writing efficient parallel code, especially when dealing with spatial datasets. Two(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)
In this paper we propose a technique called storage-aware spatial prefetching that can provide significant performance improvements for out-of-core visualization. This approach is motivated by file chunking in which a multidimensional data file is reorganized into multidimensional sub-blocks that are stored linearly in the file. This increases the(More)