NDLI: Fast and Robust Parallel SGD Matrix Factorization

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Fast and Robust Parallel SGD Matrix Factorization

Content Provider	ACM Digital Library
Author	Jiang, Xiaoqian Han, Wook-Shin Oh, Jinoh Yu, Hwanjo
Abstract	Matrix factorization is one of the fundamental techniques for analyzing latent relationship between two entities. Especially, it is used for recommendation for its high accuracy. Efficient parallel SGD matrix factorization algorithms have been developed for large matrices to speed up the convergence of factorization. However, most of them are designed for a shared-memory environment thus fail to factorize a large matrix that is too big to fit in memory, and their performances are also unreliable when the matrix is skewed. This paper proposes a fast and robust parallel SGD matrix factorization algorithm, called MLGF-MF, which is robust to skewed matrices and runs efficiently on block-storage devices (e.g., SSD disks) as well as shared-memory. MLGF-MF uses Multi-Level Grid File (MLGF) for partitioning the matrix and minimizes the cost for scheduling parallel SGD updates on the partitioned regions by exploiting partial match queries processing}. Thereby, MLGF-MF produces reliable results efficiently even on skewed matrices. MLGF-MF is designed with asynchronous I/O permeated in the algorithm such that CPU keeps executing without waiting for I/O to complete. Thereby, MLGF-MF overlaps the CPU and I/O processing, which eventually offsets the I/O cost and maximizes the CPU utility. Recent flash SSD disks support high performance parallel I/O, thus are appropriate for executing the asynchronous I/O. From our extensive evaluations, MLGF-MF significantly outperforms (or converges faster than) the state-of-the-art algorithms in both shared-memory and block-storage environments. In addition, the outputs of MLGF-MF is significantly more robust to skewed matrices. Our implementation of MLGF-MF is available at http://dm.postech.ac.kr/MLGF-MF as executable files.
Starting Page	865
Ending Page	874
Page Count	10
File Format	PDF
ISBN	9781450336642
DOI	10.1145/2783258.2783322
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Matrix factorization Stochastic gradient descent
Content Type	Text
Resource Type	Article

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