Abstract
In this paper we describe a new parallel Frequent Itemset Mining algorithm called “Frontier Expansion.” This implementation is optimized to achieve high performance on a heterogeneous platform consisting of a shared memory multiprocessor and multiple Graphics Processing Unit (GPU) coprocessors. Frontier Expansion is an improved data-parallel algorithm derived from the Equivalent Class Clustering (Eclat) method, in which a partial breadth-first search is utilized to exploit maximum parallelism while being constrained by the available memory capacity. In our approach, the vertical transaction lists are represented using a “bitset” representation and operated using wide bitwise operations across multiple threads on a GPU. We evaluate our approach using four NVIDIA Tesla GPUs and observed a 6–30× speedup relative to state-of-the-art sequential Eclat and FPGrowth implementations executed on a multicore CPU.
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This material is based upon work supported by the National Science Foundation under Grant Nos. CCF-0844951 and CCF-091560.
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Zhang, F., Zhang, Y. & Bakos, J.D. Accelerating frequent itemset mining on graphics processing units. J Supercomput 66, 94–117 (2013). https://doi.org/10.1007/s11227-013-0887-x
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DOI: https://doi.org/10.1007/s11227-013-0887-x