Study of Scalable Declustering Algorithms for Parallel Grid Files

Efficient storage and retrieval of large multidimensional datasets is an important concernfor large-scale scientific computations such as long-running time-dependent simulations which periodically generate snapshots of the state. The main challenge for efficiently handling such datasets is to minimize responsetime for multidimensionalrange queries. The grid file is one of the well known access methods for multidimensional and spatial data. We investigate effective and scalable declustering techniquesfor grid files with the primary goal of minimizing response time and the secondary goal of maximizing the fairness of data distribution. The main contributions of this paper are (1) analytic and experimental evaluation of existing index-based declustering techniques and their extensionsfor grid files, and (2) developmentof a proximity-based declustering algorithm called minimax which is experimentally shown to scale and to consistently achieve better response time compared to available alg...

• Type: Conference
• Year: 1996
• Where: IPPS
• Authors: Bongki Moon, Anurag Acharya, Joel H. Saltz
• Keywords: Distributed And Parallel Computing, Grid Files, IPPS 1996, Large Multidimensional Datasets, Response Time