Arindam Chatterjee

Performance analysis of a parallel distributive join algorithm on the Intel paragon

In this paper, we analyze the performance of the parallel distributive join algorithm that we proposed previously (1996). We implemented the algorithm on an Intel Paragon machine and analyzed the effect of the number of processors and the join selectivity on the performance of the algorithm. We also compared the performance of the distributed join (DJ) algorithm with that of the hybrid-hash (HH) join algorithm. Our results show that the DJ performs comparably with the HH over the entire range of number of processors used and different join selectivities. A big advantage of the parallel DJ algorithm over the RH join algorithm is that it can easily support non-equijoin operations. The results can also be used to estimate the performance of file I/O intensive applications to be implemented on the Intel Paragon machine.

An Adaptive Parallel Distributive Join Algorithm on a Cluster of Workstations

In this paper, we present an adaptive version of the parallel Distributive Join (DJ) algorithm that we proposed in [5]. The adaptive parallel DJ algorithm can handle the data skew in operand relations efficiently. We implemented the original and adaptive parallel DJ algorithms on a network of Alpha workstations using the Parallel Virtual Machine (PVM). We analyzed the performance of the algorithms, and compared it with that of the parallel Hybrid-Hash (HH) join algorithms. Our results show that the parallel DJ algorithms perform comparably with the parallel HH join algorithms over the entire range of the number of processors used and for different join selectivities. A significant advantage of the parallel DJ algorithms is that they can easily support non-equijoin operations.

Adaptive parallel distributive join algorithm for skewed data

We present an adaptive version of the parallel distributive join (DJ) algorithm that we proposed in (Chung and Yang, 1996). The adaptive parallel DJ algorithm can handle the data skew in operand relations efficiently. We implemented the original and adaptive parallel DJ algorithms on a network of Alpha workstations using the Parallel Virtual Machine (PVM). We analyzed the performance of the algorithms, and compared it with that of the parallel Hybrid-Hash (HH) join algorithms. Our results show that the parallel DJ algorithms perform comparably with the parallel HH join algorithms over the entire range of the number of processors used and for different join selectivities. A significant advantage of the parallel DJ algorithms is that they can easily support non-equijoin operations.

Parallel Distributive Join Algorithm on the Intel Paragon

In this paper, we analyze the performance of the parallel Distributive Join algorithm that we proposed in Chung and Yang 1995. We implemented the algorithm on an Intel Paragon machine and analyzed the effect of the number of processors and the join selectivity on the performance of the algorithm. We also compared the performance of the Distributive Join (DJ) algorithm with that of the Hybrid-Hash(HH) join algorithm. Our results show that the DJ performs comparably with the HH over the entire range of number of processors used and different join selectivities. A big advantage of the parallel DJ algorithm over the HH join algorithm is that it can easily support non-equijoin operations. The results can also be used to estimate the performance of file I/O intensive applications to be implemented on the Intel Paragon machine.