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Abstract: With recent technological advances, shared memory parallel machines have become more scalable, and offer large main memories and high bus bandwidths. They are emerging as good platforms for data warehousing and data mining. In this paper, we focus on shared memory parallelization of data mining algorithms. We have developed a series of techniques for parallelization of data mining algorithms, including full replication, full locking, fixed locking, optimized full locking, and cache-sensitive ... (Update)
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...is of significant interest. In our previous work, we have developed several techniques for parallelizing random write reductions [10, 11]. One of the tech niques involves creating a copy of the reduction object for each thread and is referred to as full replication. The...
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BibTeX entry: (Update)
Ruoming Jin and Gagan Agrawal. Shared Memory Parallelization of Data Mining Algorithms: Techniques, Programming Interface, and Performance. In Proceedings of the second SIAM conference on Data Mining, April 2002. http://citeseer.ist.psu.edu/jin04shared.html More
@misc{ jin02shared,
author = "R. Jin and G. Agrawal",
title = "Shared Memory Parallelization of Data Mining Algorithms: Techniques",
text = "Ruoming Jin and Gagan Agrawal. Shared Memory Parallelization of Data Mining
Algorithms: Techniques, Programming Interface, and Performance. In Proceedings
of the second SIAM conference on Data Mining, April 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/jin04shared.html" }
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