Memory Placement Techniques for Parallel Association Mining

Srinivasan Parthasarathy, Mohammed J. Zaki, and Wei Li

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.

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