B/sup Mad/-Tree: an Efficient Data Structure for Parallel Processing

B-trees are used for accessing large database files, stored in lexicographic order on the secondary storage devices. Algorithms for concurrent B-tree data structures achieve only limited speedup when implemented on a parallel computer. To improve the performance, we propose a variant of the B/sup link/-tree, called the B/sup mad/-tree, which allows insertion without node splits, with multiple access in its leaf nodes, and dilation in both the index and the leaf nodes. Parallel algorithms for search, insert and restructuring are designed for partitioned, locked and distributed models. Only part of an insertion node is locked during the insert, and simultaneous insertions by multiple processors in the same node are allowed. A restructuring algorithm runs periodically in the background and requires at most one wait by any search or update operation. Our implementations demonstrate that the B/sup mad/-tree algorithms outperform the best known B/sup link/-trees, and compare favorably with linear hashing. We achieve good speedup (e.g., 4.79 with 8 processors) for partitioned algorithms, and moderate speedup (2.49 with 8 processors) for locked algorithms, even including overhead costs. The insert times obtained for B/sup mad/-trees are 50% to 60% less than that for the B/sup link/-trees in partitioned implementations, and 70% to 80% less in locked implementations. The speedup results on the distributed memory platform (a network of workstations) were not that encouraging due to high communication costs.