During the past, parallel database systems have gained increased popularity due to their high performance, scalability characteristics. with the predicted future database sizes and the complexity of queries, the scalability of these systems to hundreds and thousands of processors is essential for satisfying the projected demand. several studies have repeatedly demonstrated that both the performance and scalability of a parallel database systems in contingent on the physcial layout of data across the processors of the system. if the data is not declus tered properly, the excution of an operator might waste reources, reducing the overall processing capability of the systems. With earliner, single attribute declustering strategies, such as those found in tandem, teradata, gamma, and bubba paralel database systems, a selection query including a range predicate on any attribute other than the partitioning attribute must be sent to all processors containing tuples of the ralation. by directing a query with minimal resource requirements to processors that contain no relevant tuples, the system wastes CPU cyles, communication bandwidth, and I/O bandwidth, reducing its overall processing capability. as a solution several multi-attribute declustering strategies have been proposed. however, the performance of these declustering techniques have not previously been compared to one another nor with a single attribute partioning strategy. this paper, compares the performance of mult-attribute Grld deClustering (MAGIC) strategy and Bubba's extended range declustering (BERD) strategy and one another and with range partitioning strategy. our results indicate that MAGIC outperforms both range and BERD in all expriments conducted in this study.