Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization

We develop the first distributed -memory parallel implementation of Symmetric Nonnegative Matrix Factorization (SymNMF), a key data analytics kernel 14 clustering and dimensionality reduction. Our implementation includes two different algorithms for SytnNMF, 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 nonsymmetric NNW 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.