Shared-Memory Parallelization of FP-growth with Dynamic Load Estimation and Balancing
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- SAKURAI Kentaro
- Meijo University
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- KAMEYA Yoshitaka
- Meijo University
Bibliographic Information
- Other Title
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- 動的なタスク量推定と負荷分散を備えた FP-growth 法の共有メモリ型並列化
Description
<p>Although FP-growth is known to be an efficient frequent pattern mining algorithm, it is still a problem how to make it work for huge transactional databases. In this paper, we propose a shared-memory, task parallelization method for FP-growth. In the proposed method, each task receives conditional transactions from the current FP-tree, builds a conditional FP-tree, and generates the next tasks for the successive branches in the search tree. Then, we dynamically estimate the loads of such next tasks based on the corresponding conditional FP-trees and balance them among CPU cores in the manner of work-stealing. Furthermore, in order to exploit computational resources in a light-weight way, we implement the proposed method in Rust, a compiler language that can handle memory safely without garbage collection. In the most of benchmark datasets we tested, a performance improvement in parallelization was generally observed in comparison with Zaiane et al.'s simple method.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2021 (0), 2H1GS3a04-2H1GS3a04, 2021
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390851320454027392
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- NII Article ID
- 130008051574
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed