@inproceedings{f8cb67620f114d6486ce9faf84d59daf,
title = "Fast Frequent Pattern Mining without Candidate Generations on GPU by Low Latency Memory Allocation",
abstract = "In this work, we propose a GPU-accelerated algorithm for frequent pattern(FP) mining without candidate generation. We observe that the existing FP-growth algorithm has critical characteristics unsuitable for GPU, including the tree data structure, deep recursion and heavy dynamic memory allocations. By utilizing iterative execution and collectively allocating memory on GPU, our proposed method significantly reduce the latency caused by large memory allocations of original FP-growth. Experiment results show that our solution outperforms baselines, including sequential FP-growth with CPU only and existing GPU-accelerated Apriori and FP-growth, on various data sets with a significant speedup, from several times to hundred times.",
keywords = "Apriori, FP-growth, Frequent itemsets mining, Frequent pattern mining, GPGPU, GPU-accelerating",
author = "Yu-Chen Wu and Mi-Yen Yeh and Tei-Wei Kuo",
year = "2019",
month = dec,
doi = "10.1109/BigData47090.2019.9006541",
language = "English",
series = "Proceedings - IEEE International Conference on Big Data, Big Data ",
publisher = "IEEE",
pages = "1407--1416",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Xiaohua Hu and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, \{Yanfang Fanny\}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data",
address = "United States",
note = "2019 IEEE International Conference on Big Data (Big Data 2019) ; Conference date: 09-12-2019 Through 12-12-2019",
}