Abstract
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.
- Developers:
-
Austin, Woody [1] ; Klinvex, Alicia [1] ; Ballard, Grey [2] ; Kolda, Tamara [1]
- Sandia National Laboratories
- Wake Forest University
- Release Date:
- 2016-09-21
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
C++
Shell
CMake
MATLAB
Makefile
Python
- Version:
- 1.0
- Licenses:
-
BSD 2-clause "Simplified" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC04-94AL85000
- Code ID:
- 45231
- Site Accession Number:
- SCR #2148.0; 7258
- Research Org.:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Country of Origin:
- United States
Citation Formats
Austin, Woody, Klinvex, Alicia, Ballard, Grey, and Kolda, Tamara G.
TuckerCompressMPI v. 1.0.
Computer Software.
https://github.com/sandialabs/TuckerMPI.
USDOE.
21 Sep. 2016.
Web.
doi:10.11578/dc.20201001.6.
Austin, Woody, Klinvex, Alicia, Ballard, Grey, & Kolda, Tamara G.
(2016, September 21).
TuckerCompressMPI v. 1.0.
[Computer software].
https://github.com/sandialabs/TuckerMPI.
https://doi.org/10.11578/dc.20201001.6.
Austin, Woody, Klinvex, Alicia, Ballard, Grey, and Kolda, Tamara G.
"TuckerCompressMPI v. 1.0." Computer software.
September 21, 2016.
https://github.com/sandialabs/TuckerMPI.
https://doi.org/10.11578/dc.20201001.6.
@misc{
doecode_45231,
title = {TuckerCompressMPI v. 1.0},
author = {Austin, Woody and Klinvex, Alicia and Ballard, Grey and Kolda, Tamara G.},
abstractNote = {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.},
doi = {10.11578/dc.20201001.6},
url = {https://doi.org/10.11578/dc.20201001.6},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20201001.6}},
year = {2016},
month = {sep}
}