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KDD
1998
ACM

Memory Placement Techniques for Parallel Association Mining

9 years 2 months ago
Memory Placement Techniques for Parallel Association Mining
Many data mining tasks (e.g., Association Rules, Sequential Patterns) use complex pointer-based data structures (e.g., hash trees) that typically suffer from sub-optimal data locality. In the multiprocessor case shared access to these data structures may also result in false sharing. For these tasks it is commonly observed that the recursive data structure is built once and accessed multiple times during each iteration. Furthermore, the access patterns after the build phase are highly ordered. In such cases locality and false sharing sensitive memory placement of these structures can enhance performance significantly. We evaluate a set of placement policies for parallel association discovery, and show that simple placement schemes can improve execution time by more than a factor of two. More complex schemes yield additional gains.
Srinivasan Parthasarathy, Mohammed Javeed Zaki, We
Added 06 Aug 2010
Updated 06 Aug 2010
Type Conference
Year 1998
Where KDD
Authors Srinivasan Parthasarathy, Mohammed Javeed Zaki, Wei Li
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