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pnnl/HiParTI

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/HiParTI
https://doi.org/10.11578/dc.20240614.152

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Abstract

A Hierarchical Parallel Tensor Infrastructure (HiParTI), is to support fast essential sparse tensor operations and tensor decompositions on multicore CPU and GPU architectures. It consists of sparse tensor decompositions, CANDECOMP/PARAFAC (CP) and Tucker decompositions, fundamental tensor operations, and tensor transformations
Developers:
Li, Jiajia [1] ;  Central, PNNL Developer [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Pacific Northwest National Laboratory
Release Date:
2021-01-13
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Version:
1
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
Code ID:
49911
Site Accession Number:
Battelle IPID 32064-E
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/HiParTI
https://doi.org/10.11578/dc.20240614.152

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Citation Formats

Li, Jiajia, and Central, PNNL Developer. pnnl/HiParTI. Computer Software. https://github.com/pnnl/HiParTI. USDOE. 13 Jan. 2021. Web. doi:10.11578/dc.20240614.152.
Li, Jiajia, & Central, PNNL Developer. (2021, January 13). pnnl/HiParTI. [Computer software]. https://github.com/pnnl/HiParTI. https://doi.org/10.11578/dc.20240614.152.
Li, Jiajia, and Central, PNNL Developer. "pnnl/HiParTI." Computer software. January 13, 2021. https://github.com/pnnl/HiParTI. https://doi.org/10.11578/dc.20240614.152.
@misc{ doecode_49911,
title = {pnnl/HiParTI},
author = {Li, Jiajia and Central, PNNL Developer},
abstractNote = {A Hierarchical Parallel Tensor Infrastructure (HiParTI), is to support fast essential sparse tensor operations and tensor decompositions on multicore CPU and GPU architectures. It consists of sparse tensor decompositions, CANDECOMP/PARAFAC (CP) and Tucker decompositions, fundamental tensor operations, and tensor transformations},
doi = {10.11578/dc.20240614.152},
url = {https://doi.org/10.11578/dc.20240614.152},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240614.152}},
year = {2021},
month = {jan}
}