DocumentCode :
3259526
Title :
Adaptive parallel distributive join algorithm for skewed data
Author :
Chung, Soon M. ; Chatterjee, Arindam
Author_Institution :
Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
fYear :
2001
fDate :
2001
Firstpage :
15
Lastpage :
22
Abstract :
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
Keywords :
parallel algorithms; relational algebra; relational databases; software performance evaluation; workstation clusters; Alpha workstation network; PVM; Parallel Virtual Machine; adaptive parallel distributive join algorithm; algorithm performance; data skew; nonequijoin operations; operand relations; parallel Hybrid-Hash join algorithms; relational algebra; Algorithm design and analysis; Clustering algorithms; Computer science; Delay; Hardware; Parallel algorithms; Partitioning algorithms; Performance analysis; Virtual machining; Workstations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Systems, 2001. ICPADS 2001. Proceedings. Eighth International Conference on
Conference_Location :
Kyongju City
ISSN :
1521-9097
Print_ISBN :
0-7695-1153-8
Type :
conf
DOI :
10.1109/ICPADS.2001.934796
Filename :
934796
Link To Document :
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