45231 SNL true https://github.com/sandialabs/TuckerMPI OS S 2016-09-21 TuckerCompressMPI v. 1.0 TuckerCompressMPI https://doi.org/10.11578/dc.20201001.6 As 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. 1.0 United States Sandia National Laboratories SCR #2148.0; 7258 2020-10-01 2024-07-19 false copyrightadmin@sandia.gov false woodynaustin@gmail.com Woody Austin Sandia National Laboratories amklinv@sandia.gov Alicia Klinvex Sandia National Laboratories ballard@wfu.edu Grey Ballard Wake Forest University tgkolda@sandia.gov Tamara Kolda G. Sandia National Laboratories USDOE AC04-94AL85000 true Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States) true C++ Shell CMake MATLAB Makefile Python BSD 2-clause "Simplified" License