%0Computer Program
%TTuckerCompressMPI v. 1.0
%XAs parallel computing trends towards the exascale, scientific data produced by high-fidelity simulations are growing increasingly massive. For instance, a simulation on a three-dimensional spatial grid with 512 points per dimension that tracks 64 variables per grid point for 128 time steps yields 8 TB of data. By viewing the data as a dense five-way tensor, we can compute a Tucker decomposition to find inherent low-dimensional multilinear structure, achieving compression ratios of up to 10000 on real-world data sets with negligible loss in accuracy. So that we can operate on such massive data, we present the first-ever distributed-memory parallel implementation for the Tucker decomposition, whose key computations correspond to parallel linear algebra operations, albeit with nonstandard data layouts. Our approach specifies a data distribution for tensors that avoids any tensor data redistribution, either locally or in parallel. This software provides a method for compressing large-scale multiway data.
%AAustin, Woody
%AKlinvex, Alicia
%ABallard, Grey
%AKolda, Tamara
%Rhttps://doi.org/10.11578/dc.20201001.6
%Uhttps://www.osti.gov/doecode/biblio/45231
%CUnited States
%D2016
%GEnglish
%2USDOE
%1AC04-94AL85000
2016-09-21