{"id":19863747,"url":"https://github.com/sandialabs/sparten","last_synced_at":"2026-06-13T05:31:45.779Z","repository":{"id":142395587,"uuid":"588756687","full_name":"sandialabs/sparten","owner":"sandialabs","description":null,"archived":false,"fork":false,"pushed_at":"2023-01-14T08:09:48.000Z","size":3548,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-01T00:25:56.676Z","etag":null,"topics":["count-data","low-rank-tensor-decomposition","scr-2235","snl-data-analysis","tensor"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sandialabs.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-01-13T23:11:17.000Z","updated_at":"2024-09-03T01:12:11.000Z","dependencies_parsed_at":null,"dependency_job_id":"4f278090-c9cc-4bf1-bba3-0712b2647992","html_url":"https://github.com/sandialabs/sparten","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/sandialabs/sparten","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fsparten","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fsparten/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fsparten/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fsparten/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sandialabs","download_url":"https://codeload.github.com/sandialabs/sparten/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fsparten/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34273788,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-13T02:00:06.617Z","response_time":62,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["count-data","low-rank-tensor-decomposition","scr-2235","snl-data-analysis","tensor"],"created_at":"2024-11-12T15:15:56.335Z","updated_at":"2026-06-13T05:31:45.761Z","avatar_url":"https://github.com/sandialabs.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SparTen: Software for Sparse Tensor Decompositions\n\nSparTen is a set of C++ tools that provide capabilities for generating\nsparse count tensor data and computing low-rank canonical polyadic\n(CP) decompositions.\n\n```\nSandia National Laboratories is a multimission laboratory managed\nand operated by National Technology and Engineering Solutions of Sandia,\nLLC, a wholly owned subsidiary of Honeywell International, Inc., for the\nU.S. Department of Energy's National Nuclear Security Administration under\ncontract DE-NA0003525.\n\nCopyright 2017 National Technology \u0026 Engineering Solutions of Sandia, LLC\n(NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S.\nGovernment retains certain rights in this software.\n```\n\n**Main point of contact:** Danny Dunlavy (dmdunla@sandia.gov)\n\n## Downloading SparTen git submodules\n\nSparTen includes git submodules that must be retrieved as follows \nbefore building SparTen:\n\n```\ngit submodule update --init --recursive\n```\n\n## Building SparTen\n\nSee [BUILD.md](BUILD.md) for building serial, OpenMP, or NVIDIA GPU\nversions.\n\n## Running SparTen\n\nExamples below assume you are running SparTen from a directory where\nyou built SparTen using the instructions above.\n\n### Getting help\n\n```\n./bin/Sparten_main --help\n```\n\n### Using example test data\n\n```\n./bin/Sparten_main \\\n    --rank 3 \\\n    --input $PWD/test/data/cpapr_test_10x10x10_1e+06/tensor.txt \\\n    --output $PWD/cpapr_test_10x10x10.ktns\n```\n\n### Creating and using randomly generated tensor data\n\nCreate data:\n\n```\n./bin/Sparten_tensor_gen \\\n    --num-components 5 \\\n    --max-num-nonzeros 100 \\\n    --dim-sizes \"10,20,30\" \\\n    --sptensor-output-file $PWD/cpapr_10x20x30_100.tns \\\n    --ktensor-output-file $PWD/cpapr_10x20x30_100.gen.ktns\n```\n\nRun SparTen:\n\n```\n./bin/Sparten_main \\\n    --rank 3 \\\n    --input $PWD/cpapr_10x20x30_100.tns \\\n    --output $PWD/cpapr_10x20x30_100.gen.ktns\n```\n\n## Citing SparTen\n\nIf you use SparTen in your work, please cite the following:\n\nKeita Teranishi, Daniel M. Dunlavy, Jeremy M. Myers and Richard F. Barrett, \n\"SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data,\" \n2020 IEEE High Performance Extreme Computing Conference (HPEC), \nWaltham, MA, USA, 2020, pp. 1-7, \nhttps://doi.org/10.1109/HPEC43674.2020.9286251.\n\n```\n@INPROCEEDINGS{SparTen,\n  author={Teranishi, Keita and Dunlavy, Daniel M. and Myers, Jeremy M. and Barrett, Richard F.},\n  booktitle={2020 IEEE High Performance Extreme Computing Conference (HPEC)}, \n  title={SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data}, \n  year={2020},\n  pages={1-7},\n  doi={10.1109/HPEC43674.2020.9286251}}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fsparten","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandialabs%2Fsparten","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fsparten/lists"}