publications

2024

1. Clustering and Topic Discovery of Multiway Data via Joint-NCMTF

   Benjamin Cobb, Ricardo Velasquez, Richard Vuduc, and 1 more author

   In 2024 IEEE International Conference on Big Data (BigData), 2024

   DOI Code
2. On Rank Selection for Nonnegative Matrix Factorization

   Srinivas Eswar, Koby Hayashi, Benjamin Cobb, and 4 more authors

   In 2024 IEEE International Conference on Big Data (BigData), 2024

   DOI Code

2023

1. Distributed-Memory Parallel JointNMF

   Srinivas Eswar, Benjamin Cobb, Koby Hayashi, and 4 more authors

   In Proceedings of the 37th ACM International Conference on Supercomputing, Orlando, FL, USA, 2023

   Abs DOI Video Code

   Joint Nonnegative Matrix Factorization (JointNMF) is a hybrid method for mining information from datasets that contain both feature and connection information. We propose distributed-memory parallelizations of three algorithms for solving the JointNMF problem based on Alternating Nonnegative Least Squares, Projected Gradient Descent, and Projected Gauss-Newton. We extend well-known communication-avoiding algorithms using a single processor grid case to our coupled case on two processor grids. We demonstrate the scalability of the algorithms on up to 960 cores (40 nodes) with 60% parallel efficiency. The more sophisticated Alternating Nonnegative Least Squares (ANLS) and Gauss-Newton variants outperform the first-order gradient descent method in reducing the objective on large-scale problems. We perform a topic modelling task on a large corpus of academic papers that consists of over 37 million paper abstracts and nearly a billion citation relationships, demonstrating the utility and scalability of the methods.

2022

1. FIST-HOSVD: fused in-place sequentially truncated higher order singular value decomposition

   Benjamin Cobb, Hemanth Kolla, Eric Phipps, and 1 more author

   In Proceedings of the Platform for Advanced Scientific Computing Conference, Basel, Switzerland, 2022

   Abs DOI Video Code

   In this paper, several novel methods of improving the memory locality of the Sequentially Truncated Higher Order Singular Value Decomposition (ST-HOSVD) algorithm for computing the Tucker decomposition are presented. We show how the two primary computational kernels of the ST-HOSVD can be fused together into a single kernel to significantly improve memory locality. We then extend matrix tiling techniques to tensors to further improve cache utilization. This block-based approach is then coupled with a novel in-place transpose algorithm to drastically reduce the memory requirements of the algorithm by overwriting the original tensor with the result. Our approach’s effectiveness is demonstrated by comparing the multi-threaded performance of our optimized ST-HOSVD algorithm to TuckerMPI, a state-of-the-art ST-HOSVD implementation, in compressing two combustion simulation datasets. We demonstrate up to   135x reduction in auxiliary memory consumption thereby increasing the problem size that can be computed for a given memory allocation by up to   3x, whilst maintaining comparable runtime performance.