TY - GEN
T1 - Distributed-memory parallel symmetric nonnegative matrix factorization
AU - Eswar, Srinivas
AU - Hayashi, Koby
AU - Ballard, Grey
AU - Kannan, Ramakrishnan
AU - Vuduc, Richard
AU - Park, Haesun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - High performance computing
KW - Newton method
KW - Parallel algorithms
KW - Symmetric Matrices
UR - http://www.scopus.com/inward/record.url?scp=85102383760&partnerID=8YFLogxK
U2 - 10.1109/SC41405.2020.00078
DO - 10.1109/SC41405.2020.00078
M3 - Conference contribution
AN - SCOPUS:85102383760
T3 - International Conference for High Performance Computing, Networking, Storage and Analysis, SC
BT - Proceedings of SC 2020
PB - IEEE Computer Society
T2 - 2020 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020
Y2 - 9 November 2020 through 19 November 2020
ER -