{"id":28320309,"url":"https://github.com/amd/amd-lab-notes","last_synced_at":"2025-10-11T10:14:10.920Z","repository":{"id":62139520,"uuid":"554559926","full_name":"amd/amd-lab-notes","owner":"amd","description":"AMD lab notes with code examples to demonstrate use of AMD GPUs","archived":false,"fork":false,"pushed_at":"2024-06-28T21:00:00.000Z","size":11900,"stargazers_count":101,"open_issues_count":1,"forks_count":13,"subscribers_count":22,"default_branch":"release","last_synced_at":"2025-09-09T05:17:50.078Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/amd.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","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":null,"agents":null,"dco":null,"cla":null}},"created_at":"2022-10-20T02:26:59.000Z","updated_at":"2025-08-06T08:34:36.000Z","dependencies_parsed_at":"2023-02-01T08:00:25.140Z","dependency_job_id":"75045c7e-325f-41da-ab7f-4a20d6301462","html_url":"https://github.com/amd/amd-lab-notes","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/amd/amd-lab-notes","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amd%2Famd-lab-notes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amd%2Famd-lab-notes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amd%2Famd-lab-notes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amd%2Famd-lab-notes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/amd","download_url":"https://codeload.github.com/amd/amd-lab-notes/tar.gz/refs/heads/release","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amd%2Famd-lab-notes/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279006838,"owners_count":26084203,"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":"2025-10-11T02:00:06.511Z","response_time":55,"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":"2025-05-25T10:08:50.709Z","updated_at":"2025-10-11T10:14:10.915Z","avatar_url":"https://github.com/amd.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003c!---\nCopyright (c) 2022 Advanced Micro Devices, Inc. (AMD)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n---\u003e\n# AMD lab notes\n\nComputational and Data science have emerged as powerful modes of scientific inquiry and engineering design. \nOften referred to as the \"third\" and \"fourth\" pillars of the scientific method, they are interdisciplinary \nfields where computer models and simulations of physical, biological, or data-driven processes are used to \nprobe, predict, and analyze complex systems of interest.\nAll of this necessitates the use of more computational \npower and resources to keep up with increasing scientific and industrial demands. In order to fully \nutilize emerging hardware designed to tackle these challenges, the development of robust software \nfor high-performance computing (HPC) and Machine Learning (ML) applications is now more crucial than ever. This challenge \nis made even greater as hardware trends continue to achieve massive parallelism through GPU \nacceleration, which requires the adoption of sophisticated heterogenous programming environments \nand carefully tuned application code.\n\nIn this \"AMD lab notes\" blog series, we share the lessons learned from tuning a wide range of scientific applications,\nlibraries, and frameworks for AMD GPUs.\nOur goal with these lab notes is to provide readers with the following:\n\n- AMD GPU implementations of computational science algorithms such as PDE discretizations, \nlinear algebra, solvers, and more\n- AMD GPU programming tutorials showcasing optimizations\n- Instructions for leveraging ML frameworks, data science tools, post-processing, and visualization on AMD GPUs\n- Best practices for porting and optimizing HPC and ML applications targeting AMD GPUs\n- Guidance on using libraries and tools from the ROCm™ software stack\n\nMost of our lab notes contain accompanying code examples that readers are encouraged to experiment with.\nThe intention is to provide content that targets domain experts and \ncomputational/data scientists alike. While our optimization strategies may be specific to a particular \napplication, we believe that the content can serve as loose guidelines and an effective starting point \nfor getting the best experience out of AMD hardware. We primarily focus on AMD Instinct™ GPUs,\nbut we expect users of other AMD graphics cards to also benefit from the strategies outlined in these notes.\n\nThe repository containing all lab notes and associated code examples can be found at \n[https://github.com/AMD/amd-lab-notes](https://github.com/AMD/amd-lab-notes).\nWe hope that our pedagogical examples will inspire readers to accelerate their application code even further.\n\nIf you have any questions or comments, please reach out to us on GitHub [Discussions](https://github.com/amd/amd-lab-notes/discussions)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famd%2Famd-lab-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famd%2Famd-lab-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famd%2Famd-lab-notes/lists"}