Abstract
Modern data scientists are tasked to analyze ever-growing data sets with increasingly complex relationships. Tensor decompositions have come to play a central role in identifying underlying latent structures in higher-order data. The problem of fitting tensor models to different distributions is complicated by the combinations of size, dimensionality, and sparsity present in real world data. The situation demands efficient algorithms designed for shared-memory and distributed systems. This work will present new research that tackles these challenges on several different fronts, leveraging optimizations in numerical algorithms and sparse tensor representations in heterogeneous high performance computing environments.
- Developers:
- Release Date:
- 2021-12-07
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC52-06NA25396
- Code ID:
- 64375
- Site Accession Number:
- C21067
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Country of Origin:
- United States
Citation Formats
Bhattarai, Manish, Kharat, Namita, Skau, Erik, Alexandrov, Boian, Vangara, Raviteja, Smith, James, and Tierney, Thomas.
pyDRESCALk.
Computer Software.
https://github.com/lanl/pyDRESCALk.
USDOE Laboratory Directed Research and Development (LDRD) Program.
07 Dec. 2021.
Web.
doi:10.11578/dc.20220106.2.
Bhattarai, Manish, Kharat, Namita, Skau, Erik, Alexandrov, Boian, Vangara, Raviteja, Smith, James, & Tierney, Thomas.
(2021, December 07).
pyDRESCALk.
[Computer software].
https://github.com/lanl/pyDRESCALk.
https://doi.org/10.11578/dc.20220106.2.
Bhattarai, Manish, Kharat, Namita, Skau, Erik, Alexandrov, Boian, Vangara, Raviteja, Smith, James, and Tierney, Thomas.
"pyDRESCALk." Computer software.
December 07, 2021.
https://github.com/lanl/pyDRESCALk.
https://doi.org/10.11578/dc.20220106.2.
@misc{
doecode_64375,
title = {pyDRESCALk},
author = {Bhattarai, Manish and Kharat, Namita and Skau, Erik and Alexandrov, Boian and Vangara, Raviteja and Smith, James and Tierney, Thomas},
abstractNote = {Modern data scientists are tasked to analyze ever-growing data sets with increasingly complex relationships. Tensor decompositions have come to play a central role in identifying underlying latent structures in higher-order data. The problem of fitting tensor models to different distributions is complicated by the combinations of size, dimensionality, and sparsity present in real world data. The situation demands efficient algorithms designed for shared-memory and distributed systems. This work will present new research that tackles these challenges on several different fronts, leveraging optimizations in numerical algorithms and sparse tensor representations in heterogeneous high performance computing environments.},
doi = {10.11578/dc.20220106.2},
url = {https://doi.org/10.11578/dc.20220106.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20220106.2}},
year = {2021},
month = {dec}
}