{"id":19135420,"url":"https://github.com/rkv0id/boltzmanumba","last_synced_at":"2025-09-08T19:31:12.675Z","repository":{"id":189217916,"uuid":"343377783","full_name":"rkv0id/boltzmanumba","owner":"rkv0id","description":"GPU-Parallelization of a sequential Lattice Boltzmann gist on CUDA-capable devices using Numba.","archived":false,"fork":false,"pushed_at":"2021-03-05T18:27:09.000Z","size":1120,"stargazers_count":3,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-11-09T06:38:19.532Z","etag":null,"topics":["cuda","lbm","numba"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rkv0id.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-03-01T10:35:17.000Z","updated_at":"2024-07-04T12:33:44.000Z","dependencies_parsed_at":"2023-08-18T20:55:42.515Z","dependency_job_id":null,"html_url":"https://github.com/rkv0id/boltzmanumba","commit_stats":null,"previous_names":["rkv0id/boltzmanumba"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rkv0id%2Fboltzmanumba","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rkv0id%2Fboltzmanumba/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rkv0id%2Fboltzmanumba/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rkv0id%2Fboltzmanumba/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rkv0id","download_url":"https://codeload.github.com/rkv0id/boltzmanumba/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":232337346,"owners_count":18507695,"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","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":["cuda","lbm","numba"],"created_at":"2024-11-09T06:30:13.395Z","updated_at":"2025-01-03T12:31:28.854Z","avatar_url":"https://github.com/rkv0id.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# boltzmanumba\n\u003cimg src=\"./gpu_out/vel.0019.png\"\u003e\n\nParallelization of a sequential Lattice Boltzmann code picked up from a random GitHub gist.\n\nLBM is a simulation technique for complex fluid systems. It is known to be so performance greedy while also being an embarassingly parallel algorithm. This repo is a basic attempt of distributing the LBM algorithm on a CUDA-able GPU using the [Numba API](http://numba.pydata.org).\n\nThe `lattice_gpu_naive.py` file contains a naive parallelization of the code while the `lattice_gpu_opt.py` file contains a *memory-usage-optimized* code for the same algorithm.\n\nThe notebook provided within this repository contains a benchmarking of the GPU/CPU acceleration.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frkv0id%2Fboltzmanumba","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frkv0id%2Fboltzmanumba","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frkv0id%2Fboltzmanumba/lists"}