{"data":{"slug":"merrymercy-awesome-tensor-compilers","name":"awesome-tensor-compilers","tagline":"A collection of compiler projects and papers for tensor computation and deep learning.","github_url":"https://github.com/merrymercy/awesome-tensor-compilers","owner":"merrymercy","repo":"awesome-tensor-compilers","owner_avatar_url":"https://avatars.githubusercontent.com/u/15100009?v=4","primary_language":null,"stars":2788,"forks":330,"topics":["code-generation","compiler","deep-learning","high-performance-computing","machine-learning","programming-language","tensor"],"archived":false,"github_pushed_at":"2024-10-19T14:35:00+00:00","maintenance_label":"Dormant","stars_delta_30d":18,"url":"https://www.graphcanon.com/tools/merrymercy-awesome-tensor-compilers","markdown_url":"https://www.graphcanon.com/tools/merrymercy-awesome-tensor-compilers.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/merrymercy-awesome-tensor-compilers","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=merrymercy-awesome-tensor-compilers","description":"A list of awesome compiler projects and papers for tensor computation and deep learning.","homepage_url":null,"license":null,"open_issues":5,"watchers":114,"ai_summary":"This repository contains links and descriptions to various papers focusing on compiler technology specifically tailored for tensor computations and deep learning applications. It emphasizes the use of cost models and automated optimization techniques in improving the performance of machine-learning programs.","readme_excerpt":"### Cost Model\n- [TLP: A Deep Learning-based Cost Model for Tensor Program Tuning](https://arxiv.org/abs/2211.03578) by Yi Zhai et al., ASPLOS 2023\n- [An Asymptotic Cost Model for Autoscheduling Sparse Tensor Programs](https://arxiv.org/abs/2111.14947) by Peter Ahrens et al., PLDI 2022\n- [TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/a684eceee76fc522773286a895bc8436-Abstract-round1.html) by Lianmin Zheng et al., NeurIPS 2021\n- [A Deep Learning Based Cost Model for Automatic Code Optimization](https://proceedings.mlsys.org/paper/2021/hash/3def184ad8f4755ff269862ea77393dd-Abstract.html) by Riyadh Baghdadi et al., MLSys 2021\n- [A Learned Performance Model for the Tensor Processing Unit](https://arxiv.org/abs/2008.01040) by Samuel J. Kaufman et al., MLSys 2021\n- [DYNATUNE: Dynamic Tensor Program Optimization in Deep Neural Network Compilation](https://openreview.net/forum?id=GTGb3M_KcUl) by Minjia Zhang et al., ICLR 2021\n- [MetaTune: Meta-Learning Based Cost Model for Fast and Efficient Auto-tuning Frameworks](https://arxiv.org/abs/2102.04199) by Jaehun Ryu et al., arxiv 2021\n- [Expedited Tensor Program Compilation Based on LightGBM](https://iopscience.iop.org/article/10.1088/1742-6596/2078/1/012019) by Gonghan Liu1 et al., JPCS 2021","github_created_at":"2020-06-18T20:27:47+00:00","created_at":"2026-07-11T23:39:14.128055+00:00","updated_at":"2026-10-04T06:00:27.244404+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"code-generation","name":"code-generation"},{"slug":"compiler","name":"compiler"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"high-performance-computing","name":"high-performance-computing"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"programming-language","name":"programming-language"},{"slug":"tensor","name":"tensor"}],"trust":{"provenance":{"is_fork":false,"github_id":273333711,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-10-04T06:00:26.300Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":714,"last_release_at":null,"stars_delta_30d":18,"open_issues_delta_30d":1},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:39:20.625Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-10-04T06:00:26.985Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["If you need references to papers on cost models and automated optimizations for tensor computation.","For exploring compiler projects aimed at deep learning applications, emphasizing high-performance computing techniques."],"when_not_to_use":["Avoid if focused solely on implementation without the need for theoretical background or detailed optimization methods.","Not suitable if your project requires immediate integration of a specific tensor compiler technology rather than review of existing research."],"source":"enrich:decision_facts","observed_at":"2026-07-17T03:22:03.637Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Decision-critical Facts for awesome-tensor-compilers"}]}}