The design and analysis of parallel algorithms
The design and analysis of parallel algorithms
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Proceedings of the sixteenth international conference on Very large databases
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Parallel database systems: the future of high performance database systems
Communications of the ACM
Performance comparison of join on hypercube and mesh
CSC '92 Proceedings of the 1992 ACM annual conference on Communications
An efficient permutation-based parallel range-join algorithm on N-dimensional torus computers
Information Processing Letters
Parallel k-set mutual range-join in hypercubes
Microprocessing and Microprogramming
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
A Taxonomy and Performance Model of Data Skew Effects in Parallel Joins
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
An Evaluation of Non-Equijoin Algorithms
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Efficient Parallel Permutation-Based Range-Join Algorithms on Mesh-Connected Computers
ACSC '95 Proceedings of the 1995 Asian Computing Science Conference on Algorithms, Concurrency and Knowledge
An efficient algorithm for constructing Hamiltonian paths in meshes
Parallel Computing
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In this paper, we present four efficient parallel algorithms for computing a nonequijoin, calledrange-join, of two relations on N\hbox{-}{\rm dimensional} mesh-connected computers. Range-joins of relations R and S are an important generalization of conventional equijoins and band-joins and are solved by permutation-based approaches in all proposed algorithms. In general, after sorting all subsets of both relations, the proposed algorithms permute every sorted subset of relation S to each processor in turn, where it is joined with the local subset of relation R. To permute the subsets of S efficiently, we propose two data permutation approaches, namely, the shifting approach which permutes the data recursively from lower dimensions to higher dimensions and the Hamiltonian-cycle approach which first constructs a Hamiltonian cycle on the mesh and then permutes the data along this cycle by repeatedly transferring data from each processor to its successor. We apply the shifting approach to meshes with different storage capacities which results in two different join algorithms. The Basic Shifting Join (BASHJ) algorithm can minimize the number of subsets stored temporarily at a processor, but requires a large number of data transmissions, while the Buffering Shifting Join (BUSHJ) algorithm can achieve a high parallelism and minimize the number of data transmissions, but requires a large number of subsets stored at each processor. For constructing a Hamiltonian cycle on a mesh, we propose two different methods which also result in two different join algorithms. The Recursive Hamiltonian-Cycle Join (REHCJ) algorithm uses a single processor to construct a Hamiltonian cycle recursively, while the Parallel Hamiltonian-Cycle Join (PAHCJ) algorithm uses all processors to construct a Hamiltonian cycle in parallel. We analyze and compare these algorithms. The results shows that both Hamiltonian cycle algorithms require less storage and local join operations than the shifting algorithms, but more data movement steps.