awesome-tensor-compilers
A collection of compiler projects and papers for tensor computation and deep learning.
GraphCanon updated Oct 4, 2026 · GitHub synced Oct 4, 2026
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Decision brief
Decision-critical Facts for awesome-tensor-compilers
Good fit when
- 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.
Avoid when
- 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.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (714d since push)
- As of Oct 4, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 4, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/merrymercy/awesome-tensor-compilersSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
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.
Capability facts
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Categories
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README
Cost Model TLP: A Deep Learning based Cost Model for Tensor Program Tuning by Yi Zhai et al., ASPLOS 2023 An Asymptotic Cost Model for Autoscheduling Sparse Tensor Programs by Peter Ahrens et al., PLDI 2022 TenSet: A Large scale Program Performance Dataset for Learned Tensor Comp...
For agents
This page has a .md twin and JSON over the API.