U.S. Department of Energy

Pacific Northwest National Laboratory

Large Scale Frequent Pattern Mining using MPI One-Sided Model

Publish Date: 
Tuesday, June 9, 2015
In this paper, we propose a work-stealing runtime --- Library for Work Stealing LibWS --- using MPI one-sided model for designing scalable FP-Growth --- {\em de facto} frequent pattern mining algorithm --- on large scale systems. LibWS provides locality efficient and highly scalable work-stealing techniques for load balancing on a variety of data distributions. We also propose a novel communication algorithm for FP-growth data exchange phase, which reduces the communication complexity from state-of-the-art O(p) to O(f + p/f) for p processes and f frequent attributed-ids. FP-Growth is implemented using LibWS and evaluated on several work distributions and support counts. An experimental evaluation of the FP-Growth on LibWS using 4096 processes on an InfiniBand Cluster demonstrates excellent efficiency for several work distributions (87\% efficiency for Power-law and 91% for Poisson). The proposed distributed FP-Tree merging algorithm provides 38x communication speedup on 4096 cores.
Vishnu A, and K Agarwal. 2015. "Large Scale Frequent Pattern Mining using MPI One-Sided Model." In IEEE International Conference on Cluster Computing (CLUSTER 2015), September 8-11, 2015, Chicago, Illinois, pp. 138-147. IEEE, Piscataway, NJ. doi:10.1109/CLUSTER.2015.30
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