{"id":13604977,"url":"https://github.com/MachineLearningSystem/spada-sim","last_synced_at":"2025-04-12T02:32:33.456Z","repository":{"id":185461955,"uuid":"597265581","full_name":"MachineLearningSystem/spada-sim","owner":"MachineLearningSystem","description":"The simulator for SPADA, an SpGEMM accelerator with adaptive dataflow","archived":false,"fork":true,"pushed_at":"2023-01-26T02:15:09.000Z","size":586,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2024-08-02T19:36:45.998Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"tsinghua-ideal/spada-sim","license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MachineLearningSystem.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":"2023-02-04T02:17:59.000Z","updated_at":"2023-01-14T03:54:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"55336ac7-13b5-47ca-bbc7-3f8e4284b04a","html_url":"https://github.com/MachineLearningSystem/spada-sim","commit_stats":null,"previous_names":["machinelearningsystem/spada-sim"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MachineLearningSystem%2Fspada-sim","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MachineLearningSystem%2Fspada-sim/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MachineLearningSystem%2Fspada-sim/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MachineLearningSystem%2Fspada-sim/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MachineLearningSystem","download_url":"https://codeload.github.com/MachineLearningSystem/spada-sim/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223489711,"owners_count":17153810,"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":[],"created_at":"2024-08-01T19:00:53.290Z","updated_at":"2024-11-07T09:31:22.262Z","avatar_url":"https://github.com/MachineLearningSystem.png","language":null,"readme":"# Spada simulator\n## Install\nPlease first install the [Rust toolchain](https://www.rust-lang.org/tools/install).\n\nThe simulator interacts with [python3](https://www.python.org/downloads/) for parsing sparse matrices:\n```bash\n$ python3 -m venv spadaenv\n$ source spadaenv/bin/activate\n$ pip install -U pip numpy scipy\n```\n\n## Build\n```bash\n$ cargo build --no-default-features\n```\n\n## Workload\nThe simulator accepts both MatrixMarket (.mtx) and numpy formatted matrices, with the latter ones packed as a pickle file (.pkl). The folder containing these matrices is specified in the config file under `config`.\n\n## Simulate\nFirst ensure the created python virtual environment is activated. The following command simulates SpGEMM of [cari](https://sparse.tamu.edu/Meszaros/cari) on Spada with the configuration specified in `config/config_1mb_row1.json`.\n```bash\n(spadaenv) $ ./target/debug/spada-sim accuratesimu spada ss cari config/config_1mb_row1.json\n```\n## Reference\n\nIf you use this tool in your research, please kindly cite the following paper.\n\nZhiyao Li, Jiaxiang Li, Taijie Chen, Dimin Niu, Hongzhong Zheng, Yuan Xie, and Mingyu Gao.\nSpada: Accelerating Sparse Matrix Multiplication with Adaptive Dataflow.\nIn *Proceedings of the 28th International Conference on Architectural Support for Programming Languages and Operating Systems* (ASPLOS), 2023.\n","funding_links":[],"categories":["Paper-Code"],"sub_categories":["Optimization"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMachineLearningSystem%2Fspada-sim","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FMachineLearningSystem%2Fspada-sim","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMachineLearningSystem%2Fspada-sim/lists"}