Compiler and Runtime Support for Data Intensive Computing on Multi-dimensional Data

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One of the largest and fastest-growing problems in scientific computing is the analysis and processing of very large data sets. These scientific data sets can come from long-running simulations (e.g. simulations of water pollution that create "snapshots" of the expected water conditions at later times), archives of remote sensing data (e.g. high-resolution satellite imagery), and archives of medical images (e.g. MRI scans for a patient or group of patients). These data sets are usually multi-dimensional, including spatial coordinates, time stamps, and several physical properties at each point. Several systems now support storage, retrieval, and visualization of such data sets, but few can efficiently process the data. This project will develop methods to produce efficient programs to carry out multi-dimensional data processing and analysis using a high-level parallel language.The project will attack this problem by developing runtime routines for optimizing resource usage, appropriate language extensions, and aggressive compiler optimizations for large data processing. The runtime methods will implement policies that optimize computational efficiency on a broad range of large data set analyses, taking into account the spatial structure and partitioning of the data and the computation to be performed. Incorporating these routines into the investigator's Active Data Repository will substantially generalize and improve that system. The language extensions and compiler optimizations will then make use of the runtime system to enable applications that analyze multi-dimensional data sets to be expressed at an abstract level, yet achieve high utilization of computational, storage, and communication resources.