Systems Support for High Performance I/O on Shared Storage Clusters

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Conventional solutions for I/O have attempted to provide hardwareand software parallelism via RAIDs or parallel machines/supercomputers.However, the problems associated with cost, scalability, and/or accessibility of these environments make them unattractive for widespread usage. This research addresses this importantdeficiency in high-performance I/O support, by proposing a shared storagesystem using an off-the-shelf cluster of workstations, disks, andnetworks. The proposed research goes beyond current state-of-the-art in I/O support for clusters and examines a broad spectrum ofissues related to I/O software on clusters, that includeapplication-directed, compiler-directed, and runtime system-directed optimizations. These optimizations are crucial to reduce/hide the latencies to different levels of the I/O hierarchy which will help accelerate the deployment of clusters for I/O-intensive applications.