{"metadata":{"code_id":45231,"site_ownership_code":"SNL","open_source":true,"repository_link":"https://github.com/sandialabs/TuckerMPI","project_type":"OS","software_type":"S","official_use_only":{},"developers":[{"email":"woodynaustin@gmail.com","orcid":"","first_name":"Woody","last_name":"Austin","middle_name":"","affiliations":["Sandia National Laboratories"]},{"email":"amklinv@sandia.gov","orcid":"","first_name":"Alicia","last_name":"Klinvex","middle_name":"","affiliations":["Sandia National Laboratories"]},{"email":"ballard@wfu.edu","orcid":"","first_name":"Grey","last_name":"Ballard","middle_name":"","affiliations":["Wake Forest University"]},{"email":"tgkolda@sandia.gov","orcid":"","first_name":"Tamara","last_name":"Kolda","middle_name":"G.","affiliations":["Sandia National Laboratories"]}],"contributors":[],"sponsoring_organizations":[{"organization_name":"USDOE","funding_identifiers":[],"primary_award":"AC04-94AL85000","DOE":true}],"contributing_organizations":[],"research_organizations":[{"organization_name":"Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)","DOE":true}],"related_identifiers":[],"award_dois":[],"release_date":"2016-09-21","software_title":"TuckerCompressMPI v. 1.0","acronym":"TuckerCompressMPI","doi":"https://doi.org/10.11578/dc.20201001.6","description":"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.","programming_languages":["C++","Shell","CMake","MATLAB","Makefile","Python"],"version_number":"1.0","country_of_origin":"United States","project_keywords":[],"licenses":["BSD 2-clause \"Simplified\" License"],"recipient_org":"Sandia National Laboratories","site_accession_number":"SCR #2148.0; 7258","date_record_added":"2020-10-01","date_record_updated":"2024-07-19","is_file_certified":false,"last_editor":"copyrightadmin@sandia.gov","is_limited":false,"links":[{"rel":"citation","href":"https://www.osti.gov/doecode/biblio/45231"}]}}