research-article

CuMF_SGD: Parallelized Stochastic Gradient Descent for Matrix Factorization on GPUs

Authors:

Yun LiangAuthors Info & Claims

HPDC '17: Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing

Pages 79 - 92

https://doi.org/10.1145/3078597.3078602

Published: 26 June 2017 Publication History

Abstract

Stochastic gradient descent (SGD) is widely used by many machine learning algorithms. It is efficient for big data ap- plications due to its low algorithmic complexity. SGD is inherently serial and its parallelization is not trivial. How to parallelize SGD on many-core architectures (e.g. GPUs) for high efficiency is a big challenge. In this paper, we present cuMF_SGD, a parallelized SGD solution for matrix factorization on GPUs. We first design high-performance GPU computation kernels that accelerate individual SGD updates by exploiting model parallelism. We then design efficient schemes that parallelize SGD updates by exploiting data parallelism. Finally, we scale cuMF SGD to large data sets that cannot fit into one GPU's memory. Evaluations on three public data sets show that cuMF_SGD outperforms existing solutions, including a 64- node CPU system, by a large margin using only one GPU card.

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CuMF_SGD: Parallelized Stochastic Gradient Descent for Matrix Factorization on GPUs

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HPDC '17: Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing

June 2017

254 pages

ISBN:9781450346993

DOI:10.1145/3078597

General Chairs:
Howie Huang
George Washington University, USA
,
Jon Weissman
University of Minnesota, USA
,
Program Chairs:
Adriana Iamnitchi
University of South Florida, USA
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Alexandru Iosup
Vrije Universiteit Amsterdam and Delft University of Technology, NLD

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