research-article

Frequent itemset mining on graphics processors

Authors:

Qiong LuoAuthors Info & Claims

DaMoN '09: Proceedings of the Fifth International Workshop on Data Management on New Hardware

Pages 34 - 42

https://doi.org/10.1145/1565694.1565702

Published: 28 June 2009 Publication History

Abstract

We present two efficient Apriori implementations of Frequent Itemset Mining (FIM) that utilize new-generation graphics processing units (GPUs). Our implementations take advantage of the GPU's massively multi-threaded SIMD (Single Instruction, Multiple Data) architecture. Both implementations employ a bitmap data structure to exploit the GPU's SIMD parallelism and to accelerate the frequency counting operation. One implementation runs entirely on the GPU and eliminates intermediate data transfer between the GPU memory and the CPU memory. The other implementation employs both the GPU and the CPU for processing. It represents itemsets in a trie, and uses the CPU for trie traversing and incremental maintenance. Our preliminary results show that both implementations achieve a speedup of up to two orders of magnitude over optimized CPU Apriori implementations on a PC with an NVIDIA GTX 280 GPU and a quad-core CPU.

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Cited By

Bavarsad Salehpour HHaj Seyyed Javadi HAsghari PShiri Ahmad Abadi M(2024)Improvement of Apriori Algorithm Using Parallelization Technique on Multi-CPU and GPU TopologyWireless Communications & Mobile Computing10.1155/2024/77169762024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/7716976
Shelar AKulkarni R(2022)Swarm of Honey Bees for Association Rule Mining Using CUDAInternational Journal of Software Innovation10.4018/IJSI.29799610:1(1-27)Online publication date: 6-May-2022
https://dl.acm.org/doi/10.4018/IJSI.297996
Chon KYi EKim M(2022)SGMiner: A Fast and Scalable GPU-Based Frequent Pattern Miner on SSDsIEEE Access10.1109/ACCESS.2022.317959210(62502-62519)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3179592
Show More Cited By

Index Terms

Frequent itemset mining on graphics processors
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors
  2. Parallel computing methodologies
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

DaMoN '09: Proceedings of the Fifth International Workshop on Data Management on New Hardware

June 2009

63 pages

ISBN:9781605587011

DOI:10.1145/1565694

Conference Chairs:
Peter A. Boncz
CWI
,
Kenneth A. Ross
Columbia University

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2009

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Research-article

Funding Sources

Research Grants Council, University Grants Committee, Hong Kong

Conference

DaMoN 2009

Sponsor:

SIGMOD

DaMoN 2009: Data Management on New Hardware

June 28, 2009

Rhode Island, Providence

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Overall Acceptance Rate 94 of 127 submissions, 74%

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Cited By

Bavarsad Salehpour HHaj Seyyed Javadi HAsghari PShiri Ahmad Abadi M(2024)Improvement of Apriori Algorithm Using Parallelization Technique on Multi-CPU and GPU TopologyWireless Communications & Mobile Computing10.1155/2024/77169762024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/7716976
Shelar AKulkarni R(2022)Swarm of Honey Bees for Association Rule Mining Using CUDAInternational Journal of Software Innovation10.4018/IJSI.29799610:1(1-27)Online publication date: 6-May-2022
https://dl.acm.org/doi/10.4018/IJSI.297996
Chon KYi EKim M(2022)SGMiner: A Fast and Scalable GPU-Based Frequent Pattern Miner on SSDsIEEE Access10.1109/ACCESS.2022.317959210(62502-62519)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3179592
Bustio-Martínez LCumplido RLetras MHernández-León RFeregrino-Uribe CHernández-Palancar J(2021)FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive ReviewACM Computing Surveys10.1145/347228954:9(1-35)Online publication date: 8-Oct-2021
https://dl.acm.org/doi/10.1145/3472289
Colombo ASpolon RLobato RManacero ACavenaghi M(2021)Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP2021 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC53001.2021.9631495(1-6)Online publication date: 5-Sep-2021
https://doi.org/10.1109/ISCC53001.2021.9631495
HAMDAD LBENATCHBA K(2021)Association Rules MiningSN Computer Science10.1007/s42979-021-00819-x2:6Online publication date: 9-Sep-2021
https://doi.org/10.1007/s42979-021-00819-x
Yan DQu WGuo GWang XZhou Y(2021)PrefixFPM: a parallel framework for general-purpose mining of frequent and closed patternsThe VLDB Journal10.1007/s00778-021-00687-031:2(253-286)Online publication date: 9-Aug-2021
https://doi.org/10.1007/s00778-021-00687-0
Abuzaid FKraft PSuri SGan EXu EShenoy AAnanthanarayan ASheu JMeijer EWu XNaughton JBailis PZaharia M(2020)DIFF: a relational interface for large-scale data explanationThe VLDB Journal10.1007/s00778-020-00633-6Online publication date: 30-Sep-2020
https://doi.org/10.1007/s00778-020-00633-6
Djenouri YFournier-Viger PLin JDjenouri DBelhadi A(2019)GPU-based swarm intelligence for Association Rule Mining in big databasesIntelligent Data Analysis10.3233/IDA-17378523:1(57-76)Online publication date: 20-Feb-2019
https://doi.org/10.3233/IDA-173785
Li AGeng TWang THerbordt MSong SBarker KTaufer MBalaji PPeña A(2019)BSTCProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3295500.3356169(1-30)Online publication date: 17-Nov-2019
https://dl.acm.org/doi/10.1145/3295500.3356169
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