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<p>An important aspect of database processing in parallel computer systems is the use of data parallel algorithms. Several parallel algorithms for the relational database join operation in a hypercube multicomputer system are given. The join algorithms are classified as cycling or global partitioning based on the tuple distribution method employed. The various algorithms are compared under a common framework, using time complexity analysis as well as an implementation on a 64-node NCUBE hypercube system. In general, the global partitioning algorithms demonstrate better speedup. However, the cycling algorithm can perform better than the global algorithms in specific situations, viz., when the difference in input relation cardinalities is large and the hypercube dimension is small. The usefulness of the data redistribution operation in improving the performance of the join algorithms, in the presence of uneven data partitions, is examined. The results indicate that redistribution significantly decreases the join algorithm execution times for unbalanced partitions.</p>
data redistribution algorithms; hypercubes; database processing; parallel computer systems; data parallel algorithms; parallel algorithms; relational database join operation; cycling; global partitioning; tuple distribution method; time complexity; 64-node NCUBE; data partitions; computational complexity; database theory; hypercube networks; parallel algorithms; query processing; relational databases

C. Baru and S. Padmanabhan, "Join and Data Redistribution Algorithms for Hypercubes," in IEEE Transactions on Knowledge & Data Engineering, vol. 5, no. , pp. 161-168, 1993.
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