Paper

Tensor decompositions have emerged as powerful tools of multivariate data analysis, providing the foundation of numerous analysis methods. The Tucker decomposition in particular has been shown to be quite effective at compressing high-dimensional scientific data sets. However, applying these techniques to modern scientific simulation data is challenged by the massive data volumes these codes can produce, requiring scalable tensor decomposition methods that can exploit the hybrid parallelism available on modern computing architectures, as well as support in situ processing to compute decompositions as these simulations generate data. In this work, we overcome these challenges by presenting a first-ever hybrid parallel and performance-portable approach for Tucker decomposition of both batch and streaming data. Our work is based on the TuckerMPI package, which provides scalable, distributed memory Tucker decomposition techniques, as well as prior work on a sequential streaming Tucker decomposition algorithm. We extend TuckerMPI to hybrid parallelism through the use of the Kokkos/Kokkos-Kernels performance portability packages, develop a hybrid parallel streaming Tucker decomposition algorithm, and demonstrate performance and portability of these approaches on a variety of large-scale scientific data sets on both CPU and GPU architectures.