{"id":32139012,"url":"https://github.com/codarcode/mgard","last_synced_at":"2026-02-19T02:02:15.107Z","repository":{"id":33371503,"uuid":"157610031","full_name":"CODARcode/MGARD","owner":"CODARcode","description":"MGARD: MultiGrid Adaptive Reduction of Data","archived":false,"fork":false,"pushed_at":"2026-01-02T18:24:03.000Z","size":15558,"stargazers_count":45,"open_issues_count":20,"forks_count":29,"subscribers_count":9,"default_branch":"master","last_synced_at":"2026-01-09T05:57:19.337Z","etag":null,"topics":["compression","reduction"],"latest_commit_sha":null,"homepage":null,"language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CODARcode.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"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,"zenodo":null,"notice":null,"maintainers":null,"copyright":"Copyright.txt","agents":null,"dco":null,"cla":null}},"created_at":"2018-11-14T20:57:29.000Z","updated_at":"2026-01-02T18:24:09.000Z","dependencies_parsed_at":"2023-09-28T19:33:45.999Z","dependency_job_id":"41cab83d-f489-4b9b-849a-0b3907ac4e57","html_url":"https://github.com/CODARcode/MGARD","commit_stats":{"total_commits":1163,"total_committers":23,"mean_commits":50.56521739130435,"dds":0.6156491831470335,"last_synced_commit":"d3fe5a0e1edb0f8b1f11a4e0718c945dd06e3c83"},"previous_names":[],"tags_count":9,"template":false,"template_full_name":null,"purl":"pkg:github/CODARcode/MGARD","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CODARcode%2FMGARD","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CODARcode%2FMGARD/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CODARcode%2FMGARD/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CODARcode%2FMGARD/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CODARcode","download_url":"https://codeload.github.com/CODARcode/MGARD/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CODARcode%2FMGARD/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29600845,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-19T00:59:38.239Z","status":"online","status_checked_at":"2026-02-19T02:00:07.702Z","response_time":117,"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":["compression","reduction"],"created_at":"2025-10-21T05:10:41.986Z","updated_at":"2026-02-19T02:02:15.096Z","avatar_url":"https://github.com/CODARcode.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cimg src=\"./doc/images/MGARD-logo.png\" width=\"200\" /\u003e \n\n[![build status][push workflow badge]][push workflow] [![format status][format workflow badge]][format workflow]\n\nMGARD (MultiGrid Adaptive Reduction of Data) is a technique for multilevel lossy compression and refactoring of scientific data based on the theory of multigrid methods.\nWe encourage you to [make a GitHub issue][issue form] if you run into any problems using MGARD, have any questions or suggestions, etc.\n\n\n\n[push workflow]: https://github.com/CODARcode/MGARD/actions/workflows/build.yml\n[push workflow badge]: https://github.com/CODARcode/MGARD/actions/workflows/build.yml/badge.svg\n[format workflow]: https://github.com/CODARcode/MGARD/actions/workflows/format.yml\n[format workflow badge]: https://github.com/CODARcode/MGARD/actions/workflows/format.yml/badge.svg\n[issue form]: https://github.com/CODARcode/MGARD/issues/new/choose\n\n[\u003cimg src=\"./doc/images/MGARD-familytree.png\" width=\"800\" /\u003e](./doc/images/MGARD-familytree.png)\n\nMGARD framework consists of the following modules. Please see the detailed instructions for each module to build and install MGARD.\n\n## `MGARD-CPU`: MGARD implementation for CPUs\nMGARD-CPU is design for running compression on CPUs. See detailed user guide in [here][mgard-cpu]\n\n[mgard-cpu]: doc/MGARD-CPU.md\n\n## `MGARD-CUDA`: CUDA accelerated compression\nMGARD-CUDA is designed for accelerating compression specifically using NVIDIA GPUs. See detailed user guide in [here][gpu instructions].\n\n[gpu instructions]: doc/MGARD-GPU.md\n\n## `MGARD-X`: Accelerated and portable compression\nMGARD-X is designed for portable compression on NVIDIA GPUs, AMD GPUs, and CPUs. See detailed user guide in [here][mgard_x instructions].\n\n[mgard_x instructions]: doc/MGARD-X.md\n\n## `MGARD-DR`/`MGARD-XDR`: Fine-grain progressive data reconstruction\nMGARD-DR and MGARD-XDR are designed for enabling fine-grain data refactoring and progressive data reconstruction. See detailed user guide in [here][mdr_x instructions].\n\n[mdr_x instructions]: doc/MDR-X.md\n\n## `MGARD-ROI`: Preserving Region-of-Interest\nMGARD-ROI is designed for preserving region-of-interest during data compression. See detailed user guide in [here][mgard-roi].\n\n[mgard-roi]: doc/MGARD-RoI.md\n\n## `MGARD-QOI`: Preserving Linear Quantity-of-Interest\nMGARD-QOI is designed for preserving linear quantity-of-interest during data compression. See detailed user guide in [here][mgard-qoi].\n\n[mgard-qoi]: doc/MGARD-QoI.md\n\n## `MGARD-Lambda`: Preserving Non-Linear Quantity-of-Interest\nMGARD-Lambda is designed for preserving non-linear quantity-of-interest during data compression. This is an experimental part of MGARD. Currently only support certain QoIs derived from XGC 5D data. See theory in [here][mgard-lambda-theory] and example in [here][mgard-lambda].\n\n[mgard-lambda-theory]: doc/images/post-processing.pdf\n[mgard-lambda]: ./examples/lambda\n\n## Self-describing format for compressed and refactored data\nData produced by MGARD, MGARD-X, and MDR-X are designed to follow a unified self-describing format. See format details in [here][mgard format].\n\n[mgard format]: doc/MGARD-format.md\n\n## Publications\n\n### Fundamental Theory\n* Xin Liang et al. [MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction.][mgard+] *IEEE Transactions on Computers*, 2021\n* Mark Ainsworth et al. [Multilevel Techniques for Compression and Reduction of Scientific Data—The Unstructured Case.][unstructured] *SIAM Journal on Scientific Computing*, 42 (2), A1402–A1427, 2020.\n* Mark Ainsworth et al. [Multilevel Techniques for Compression and Reduction of Scientific Data—Quantitative Control of Accuracy in Derived Quantities.][quantities] *SIAM Journal on Scientific Computing* 41 (4), A2146–A2171, 2019.\n* Mark Ainsworth et al. [Multilevel Techniques for Compression and Reduction of Scientific Data—The Multivariate Case.][multivariate] *SIAM Journal on Scientific Computing* 41 (2), A1278–A1303, 2019.\n* Mark Ainsworth et al. [Multilevel Techniques for Compression and Reduction of Scientific Data—The Univariate Case.][univariate] *Computing and Visualization in Science* 19, 65–76, 2018.\n* Ben Whitney. [Multilevel Techniques for Compression and Reduction of Scientific Data.][thesis] PhD thesis, Brown University, 2018.\n\n### Preserving Quantites of Interest (QoIs)\n* Xuan Wu et al. [Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of Interest.] [qoi] **the International Conference for High Performance Computing, Networking, Storage and Analysis 2024*, Nov, 2024* \n* Tania Banerjee et al. [Scalable Hybrid Learning Techniques for Scientific Data Compression.][pp3], *Arxiv*, 2022\n* Qian Gong et al. [Region-adaptive, Error-controlled Scientific Data Compression using Multilevel Decomposition.][roi2] *the 34th International Conference on Scientific and Statistical Database Management*, Jul. 2022\n* Tania Benerjee et al. An algorithmic and software pipeline for very large scale scientific data compression with error guarantees. *International Conference on High Performance Computing, Data, and Analytics*, 2022\n* Jaemoon Lee et al. [Error-bounded learned scientific data compression with preservation of derived quantities.][pp] *Applied Sciences*, 2022\n* Qian Gong et al. [Maintaining trust in reduction: Preserving the accuracy of quantities of interest for lossy compression.][roi] *21st Smoky Mountains Computational Sciences and Engineering Conference*, Oct. 2021\n\n### Pregressive Retrieval\n* Jinzheng Wang et al. Improving Progressive Retrieval for HPC Scientific Data using Deep Neural Network. *IEEE International Conference on Data Engineering (ICDE)*, 2023 \n* Xin Liang et al. [Error-controlled, progressive, and adaptable retrieval of scientific data with multilevel decomposition.][mdr] *the International Conference for High Performance Computing, Networking, Storage and Analysis 2021*, Nov, 2021\n\n### Parallelization and GPU Acceleration\n* Jieyang Chen et al. [HPDR: High-Performance Portable Scientific Data Reduction Framework.][gpu3] 39th IEEE International Parallel and Distributed Processing Symposium, June 3-7, 2025\n* Jieyang Chen et al. [Scalable Multigrid-based Hierarchical Scientific Data Refactoring on GPUs.][gpu2] *Arxiv*\n* Jieyang Chen et al. [Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs.][gpu] *35th IEEE International Parallel \u0026 Distributed Processing Symposium*, May 17–21, 2021.\n\n### System Optimizations\n* Lipeng Wan et al. RAPIDS: Reconciling Availability, Accuracy, and Performance in Managing Geo-Distributed Scientific Data. *the International ACM Symposium on High-Performance Parallel and Distributed Computing*, Jun. 2023\n* Xinying Wang et al. [Unbalanced Parallel I/O: An Often-Neglected Side Effect of Lossy Scientific Data Compression.][unbalanced-io] *7th International Workshop on Data Analysis and Reduction for Big Scientific Data*, Nov. 2021\n\n[thesis]: https://doi.org/10.26300/ya1v-hn97\n[univariate]: https://doi.org/10.1007/s00791-018-00303-9\n[multivariate]: https://doi.org/10.1137/18M1166651\n[quantities]: https://doi.org/10.1137/18M1208885\n[unstructured]: https://doi.org/10.1137/19M1267878\n[gpu]: https://ieeexplore.ieee.org/abstract/document/9460526/\n[gpu2]: https://arxiv.org/abs/2105.12764\n[gpu3]: https://ieeexplore.ieee.org/document/11078565\n[mgard+]: https://ieeexplore.ieee.org/abstract/document/9479913/\n[unbalanced-io]: https://ieeexplore.ieee.org/abstract/document/9652573/\n[mdr]: https://dl.acm.org/doi/abs/10.1145/3458817.3476179\n[roi]: https://link.springer.com/chapter/10.1007/978-3-030-96498-6_2\n[roi2]: https://dl.acm.org/doi/abs/10.1145/3538712.3538717\n[pp]: https://www.mdpi.com/1709018 \n[pp3]: https://arxiv.org/abs/2212.10733\n[qoi]: https://ieeexplore.ieee.org/abstract/document/10793162\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodarcode%2Fmgard","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodarcode%2Fmgard","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodarcode%2Fmgard/lists"}