SCHEDULE: NOV 16-21, 2014

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Association Rule Mining with the Micron Automata Processor

SESSION: Poster Reception

EVENT TYPE: Posters

TIME: 5:15PM - 7:00PM

AUTHOR(S):Ke Wang, Mircea Stan, Kevin Skadron

ROOM:New Orleans Theater Lobby

ABSTRACT:

Association rule mining (ARM) is a widely used data mining technique
for discovering sets of frequently associated items in large
databases. As datasets grow in size and real-time analysis becomes important,
the performance of ARM implementation can impede its applicability. We accelerate ARM by using Micron's Automata Processor (AP), a hardware implementation of
non-deterministic finite automata (NFAs), with additional features that significantly expand the AP’s capabilities beyond those of traditional NFAs. The Apriori algorithm that ARM uses for discovering itemsets maps naturally to the massive parallelism of the AP. We implement the multipass pruning strategy of Apriori through AP’s symbol replacement capability, a form of lightweight reconfigurability. 87-95X speedups are achieved by the AP-accelerated Apriori on
synthetic and real-world datasets respectively, when compared with the Apriori single-core CPU implementation. The AP-accelerated Apriori solution also outperforms multicore and GPU implementations of Eclat, a more efficient ARM algorithm, especially for large datasets.

Chair/Author Details:

Ke Wang - University of Virginia

Mircea Stan - University of Virginia

Kevin Skadron - University of Virginia

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