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Title: Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization

Abstract

We develop the first distributed-memory parallel implementation of Symmetric Nonnegative Matrix Factorization (SymNMF), a key data analytics kernel for clustering and dimensionality reduction. Our implementation includes two different algorithms for SymNMF, which give comparable results in terms of time and accuracy. The first algorithm is a parallelization of an existing sequential approach that uses solvers for non symmetric NMF. The second algorithm is a novel approach based on the Gauss-Newton method. It exploits second-order information without incurring large computational and memory costs. We evaluate the scalability of our algorithms on the Summit system at Oak Ridge National Laboratory, scaling up to 128 nodes (4,096 cores) with 70% efficiency. Additionally, we demonstrate our software on an image segmentation task.

Authors:
 [1];  [1];  [2]; ORCiD logo [3];  [4];  [4]
  1. Georgia Institute of Technology
  2. Wake Forest University, Winston-Salem
  3. ORNL
  4. Georgia Institute of Technology, Atlanta
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1798617
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: The International Conference for High Performance Computing, Networking, Storage and Analysis - Atlanta, Georgia, United States of America - 11/16/2020 10:00:00 AM-11/19/2020 10:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Eswar, Srinivas, Hayashi, Koby, Ballard, Grey, Kannan, Ramakrishnan, Vuduc, Richard, and Park, Haesun. Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization. United States: N. p., 2020. Web.
Eswar, Srinivas, Hayashi, Koby, Ballard, Grey, Kannan, Ramakrishnan, Vuduc, Richard, & Park, Haesun. Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization. United States.
Eswar, Srinivas, Hayashi, Koby, Ballard, Grey, Kannan, Ramakrishnan, Vuduc, Richard, and Park, Haesun. 2020. "Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization". United States. https://www.osti.gov/servlets/purl/1798617.
@article{osti_1798617,
title = {Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization},
author = {Eswar, Srinivas and Hayashi, Koby and Ballard, Grey and Kannan, Ramakrishnan and Vuduc, Richard and Park, Haesun},
abstractNote = {We develop the first distributed-memory parallel implementation of Symmetric Nonnegative Matrix Factorization (SymNMF), a key data analytics kernel for clustering and dimensionality reduction. Our implementation includes two different algorithms for SymNMF, which give comparable results in terms of time and accuracy. The first algorithm is a parallelization of an existing sequential approach that uses solvers for non symmetric NMF. The second algorithm is a novel approach based on the Gauss-Newton method. It exploits second-order information without incurring large computational and memory costs. We evaluate the scalability of our algorithms on the Summit system at Oak Ridge National Laboratory, scaling up to 128 nodes (4,096 cores) with 70% efficiency. Additionally, we demonstrate our software on an image segmentation task.},
doi = {},
url = {https://www.osti.gov/biblio/1798617}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {11}
}

Conference:
Other availability
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