Efficient retrieval of multidimensional datasets through parallel I/O

Many scientific and engineering applications process large multidimensional datasets. An important access pattern for these applications is the retrieval of data corresponding to ranges of values in multiple dimensions. Performance is limited by disk largely due to high disk latencies. Tiling and distributing the data across multiple disks is an effective technique for improving performance through parallel I/O. The distribution of tiles across the disks is an important factor in achieving gains. Several schemes for declustering multidimensional data to improve the performance of range queries have been proposed in the literature. We extend the class of cyclic schemes which have been developed earlier for two-dimensional data to multiple dimensions. We establish important properties of cyclic schemes, based upon which we reduce the search space for determining good declustering schemes within the class of cyclic schemes. Through experimental evaluation, we establish that the cyclic schemes are superior to other declustering schemes, including the state-of-the-art, both in terms of the degree of parallelism and robustness.