TY  - COMP
TI  - TuckerCompressMPI v. 1.0
AB  - 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.
AU  - Austin, Woody
AU  - Klinvex, Alicia
AU  - Ballard, Grey
AU  - Kolda, Tamara
DO  - https://doi.org/10.11578/dc.20201001.6
UR  - https://www.osti.gov/doecode/biblio/45231
CY  - United States
PY  - 2016
DA  - 2016-09-21
LA  - English
C1  - Research Org.: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
C2  - Sponsor Org.: USDOE
C4  - Contract Number: AC04-94AL85000
ER  -