{"id":21833670,"url":"https://github.com/upsj/ginkgo-bot-playground","last_synced_at":"2026-05-18T00:03:03.857Z","repository":{"id":45921230,"uuid":"314243218","full_name":"upsj/ginkgo-bot-playground","owner":"upsj","description":null,"archived":false,"fork":false,"pushed_at":"2024-04-09T09:35:18.000Z","size":48017,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"develop","last_synced_at":"2025-08-01T21:31:29.226Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/upsj.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":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2020-11-19T12:36:10.000Z","updated_at":"2021-11-28T09:14:10.000Z","dependencies_parsed_at":"2024-04-09T10:51:33.975Z","dependency_job_id":null,"html_url":"https://github.com/upsj/ginkgo-bot-playground","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/upsj/ginkgo-bot-playground","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/upsj%2Fginkgo-bot-playground","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/upsj%2Fginkgo-bot-playground/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/upsj%2Fginkgo-bot-playground/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/upsj%2Fginkgo-bot-playground/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/upsj","download_url":"https://codeload.github.com/upsj/ginkgo-bot-playground/tar.gz/refs/heads/develop","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/upsj%2Fginkgo-bot-playground/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269201296,"owners_count":24377460,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-07T02:00:09.698Z","response_time":73,"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":[],"created_at":"2024-11-27T19:32:38.325Z","updated_at":"2026-05-18T00:02:53.836Z","avatar_url":"https://github.com/upsj.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"![Ginkgo](/assets/logo.png)\n\n[![Build status](https://gitlab.com/ginkgo-project/ginkgo-public-ci/badges/develop/pipeline.svg)](https://github.com/ginkgo-project/ginkgo/commits/develop)\n[![OSX-build](https://github.com/ginkgo-project/ginkgo/actions/workflows/osx.yml/badge.svg)](https://github.com/ginkgo-project/ginkgo/actions/workflows/osx.yml)\n[![Windows-build](https://github.com/ginkgo-project/ginkgo/actions/workflows/windows-msvc-ref.yml/badge.svg)](https://github.com/ginkgo-project/ginkgo/actions/workflows/windows-msvc-ref.yml)\n[![codecov](https://codecov.io/gh/ginkgo-project/ginkgo/branch/develop/graph/badge.svg)](https://codecov.io/gh/ginkgo-project/ginkgo)\n[![Maintainability Rating](https://sonarcloud.io/api/project_badges/measure?project=ginkgo-project_ginkgo\u0026metric=sqale_rating)](https://sonarcloud.io/dashboard?id=ginkgo-project_ginkgo)\n[![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=ginkgo-project_ginkgo\u0026metric=reliability_rating)](https://sonarcloud.io/dashboard?id=ginkgo-project_ginkgo)\n\n[![CDash dashboard](https://img.shields.io/badge/CDash-Access-blue.svg)](https://my.cdash.org/index.php?project=Ginkgo+Project)\n[![Documentation](https://img.shields.io/badge/Documentation-latest-blue.svg)](https://ginkgo-project.github.io/ginkgo-generated-documentation/doc/develop/)\n[![License](https://img.shields.io/github/license/ginkgo-project/ginkgo.svg)](./LICENSE)\n[![c++ standard](https://img.shields.io/badge/c%2B%2B-14-blue.svg)](https://en.wikipedia.org/wiki/C%2B%2B#Standardization)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.02260/status.svg)](https://doi.org/10.21105/joss.02260)\n\nGinkgo is a high-performance linear algebra library for manycore systems, with a\nfocus on the solution of sparse linear systems. It is implemented using modern C++\n(you will need an at least C++14 compliant compiler to build it), with GPU kernels\nimplemented in CUDA, HIP, and DPC++(SYCL).\n\n\nPerformance\n-----------\n\nAn extensive database of up-to-date benchmark results is available in the\n[performance data repository](https://github.com/ginkgo-project/ginkgo-data).\nVisualizations of the database can be interactively generated using the\n[Ginkgo Performance Explorer web application](https://ginkgo-project.github.io/gpe).\nThe benchmark results are automatically updated using the CI system to always\nreflect the current state of the library.\n\nPrerequisites\n-------------\n\n### Linux and Mac OS\n\nFor Ginkgo core library:\n\n*   _cmake 3.16+_\n*   C++14 compliant compiler, one of:\n    *   _gcc 5.5+_\n    *   _clang 3.9+_\n    *   _Intel compiler 2019+_\n    *   _Apple Clang 14.0_ is tested. Earlier versions might also work.\n    *   _Cray Compiler 14.0.1+_\n    *   _NVHPC Compiler 22.7+_\n\nThe Ginkgo CUDA module has the following __additional__ requirements:\n\n*   _cmake 3.18+_ (If CUDA was installed through the NVIDIA HPC Toolkit, we require _cmake 3.22+_)\n*   _CUDA 10.1+_ or _NVHPC Package 22.7+_\n*   Any host compiler restrictions your version of CUDA may impose also apply\n    here. For the newest CUDA version, this information can be found in the\n    [CUDA installation guide for Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)\n    or [CUDA installation guide for Mac Os X](https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html)\n\nThe Ginkgo HIP module has the following __additional__ requirements:\n\n* _ROCm 4.5+_\n*    the HIP, hipBLAS, hipSPARSE, hip/rocRAND and rocThrust packages compiled with either:\n    * _AMD_ backend (using the `clang` compiler)\n    * _10.1 \u003c= CUDA \u003c 11_ backend\n* if the hipFFT package is available, it is used to implement the FFT LinOps.\n\nThe Ginkgo DPC++(SYCL) module has the following __additional__ requirements:\n\n* _oneAPI 2022.1+_\n* Set `dpcpp` or `icpx` as the `CMAKE_CXX_COMPILER`\n* `c++17` is used to compile Ginkgo\n* The following oneAPI packages should be available:\n    * oneMKL\n    * oneDPL\n\nThe Ginkgo MPI module has the following __additional__ requirements:\n\n* MPI 3.1+, ideally with GPUDirect support for best performance\n\nIn addition, if you want to contribute code to Ginkgo, you will also need the\nfollowing:\n\n*   _clang-format 8.0.0+_ (ships as part of _clang_)\n*   _clang-tidy_ (optional, when setting the flag `-DGINKGO_WITH_CLANG_TIDY=ON`)\n*   _iwyu_ (Include What You Use, optional, when setting the flag `-DGINKGO_WITH_IWYU=ON`)\n\n### Windows\n\n*   _cmake 3.13+_\n*   C++14 compliant 64-bit compiler:\n    *   _MinGW : gcc 5.5+_\n    *   _Microsoft Visual Studio : VS 2019+_\n\nThe Ginkgo CUDA module has the following __additional__ requirements:\n\n*   _CUDA 10.1+_\n*   _Microsoft Visual Studio_\n*   Any host compiler restrictions your version of CUDA may impose also apply\n    here. For the newest CUDA version, this information can be found in the\n    [CUDA installation guide for Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)\n\nThe Ginkgo OMP module has the following __additional__ requirements:\n*  _MinGW_\n\nIn these environments, two problems can be encountered, the solution for which is described in the\n[windows section in INSTALL.md](INSTALL.md#building-ginkgo-in-windows):\n* `ld: error: export ordinal too large` needs the compilation flag `-O1`\n* `cc1plus.exe: out of memory allocating 65536 bytes` requires a modification of the environment\n\n__NOTE:__ Some restrictions will also apply on the version of C and C++ standard\nlibraries installed on the system. This needs further investigation.\n\nQuick Install\n------------\n\n### Building Ginkgo\n\nTo build Ginkgo, you can use the standard CMake procedure.\n\n```sh\nmkdir build; cd build\ncmake -G \"Unix Makefiles\" .. \u0026\u0026 make\n```\n\nBy default, `GINKGO_BUILD_REFERENCE` is enabled. You should be able to run\nexamples with this executor. By default, Ginkgo tries to enable the relevant\nmodules depending on your machine environment (present of CUDA, ...). You can\nalso explicitly compile with the OpenMP, CUDA, HIP or DPC++(SYCL) modules enabled to\nrun the examples with these executors. Please refer to the [Installation\npage](./INSTALL.md) for more details.\n\nAfter the installation, CMake can find ginkgo with `find_package(Ginkgo)`.\nAn example can be found in the [`test_install`](test/test_install/CMakeLists.txt).\n\n### Ginkgo Examples\n\nVarious examples are available for you to understand and play with Ginkgo within the `examples/` directory. They can be compiled by passing the `-DGINKGO_BUILD_EXAMPLES=ON` to the cmake command. Documentation for the examples is available within the `doc/` folder in each of the example directory and a commented code with explanations can found in the [online documentation](https://ginkgo-project.github.io/ginkgo-generated-documentation/doc/develop/Examples.html).\n\n### Ginkgo Testing\n\nGinkgo does comprehensive unit tests using Google Tests. These tests are enabled by default and can be disabled if necessary by passing the `-DGINKGO_BUILD_TESTS=NO` to the cmake command. More details about running tests can be found in the [TESTING.md page](./TESTING.md).\n\n### Running the benchmarks\n\nA unique feature of Ginkgo is the ability to run benchmarks and view your results\nwith the help of the [Ginkgo Performance Explorer (GPE)](https://ginkgo-project.github.io/gpe/).\n\nMore details about this can be found in the [BENCHMARKING.md page](./BENCHMARKING.md)\n\nContributing to Ginkgo\n---------------------------\n\n### Contributing\n\nWhen contributing for the first time, please add yourself to the list of\nexternal contributors like in the example below.\n\n#### Contributors\nI hereby place all my contributions in this codebase under a BSD-3-Clause\nlicense, as specified in the repository's [LICENSE](./LICENSE) file.\n\nName Surname \u003cemail@domain\u003e Institution(s)\n\n#### Contributing guidelines\n\nContributing guidelines can be accessed in the [CONTRIBUTING.md\npage](./CONTRIBUTING.md). This page also contains other information useful to\ndevelopers, such as writing proper commit messages, understanding Ginkgo's\nlibrary design, relevant C++ information, and more.\n\n### Support\nIf you have any question, bug to report or would like to propose a new feature,\nfeel free to [create an\nissue on GitHub](https://github.com/ginkgo-project/ginkgo/issues/new). Another possibility\nis to send an email to [Ginkgo's main email address](mailto:ginkgo.library@gmail.com)\nor to contact any of the main [contributors](contributors.txt).\n\n\n### Licensing\n\nGinkgo is available under the [3-clause BSD license](LICENSE). All contributions\nto the project are added under this license.\n\nDepending on the configuration options used when building Ginkgo, third party\nsoftware may be pulled as additional dependencies, which have their own\nlicensing conditions. Refer to [ABOUT-LICENSING.md](ABOUT-LICENSING.md) for\ndetails.\n\nCiting Ginkgo\n-------------\n\nThe main Ginkgo paper describing Ginkgo's purpose, design and interface is\navailable through the following reference:\n\n``` bibtex\n@article{ginkgo-toms-2022,\ntitle = {{Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing}},\nvolume = {48},\ncopyright = {All rights reserved},\nissn = {0098-3500},\nshorttitle = {Ginkgo},\nurl = {https://doi.org/10.1145/3480935},\ndoi = {10.1145/3480935},\nnumber = {1},\nurldate = {2022-02-17},\njournal = {ACM Transactions on Mathematical Software},\nauthor = {Anzt, Hartwig and Cojean, Terry and Flegar, Goran and Göbel, Fritz and Grützmacher, Thomas and Nayak, Pratik and Ribizel, Tobias and Tsai, Yuhsiang Mike and Quintana-Ortí, Enrique S.},\nmonth = feb,\nyear = {2022},\nkeywords = {ginkgo, healthy software lifecycle, High performance computing, multi-core and manycore architectures},\npages = {2:1--2:33}\n}\n```\n\nFor more information on topical subjects, please refer to the [CITING.md\npage](CITING.md).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fupsj%2Fginkgo-bot-playground","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fupsj%2Fginkgo-bot-playground","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fupsj%2Fginkgo-bot-playground/lists"}