Tilus: A Tile-Level GPU Kernel Programming Language

Documentation | Paper

Tilus is a powerful research domain-specific language (DSL) for GPU programming that offers:

• Thread-block-level granularity with tensors as the primary data type.
• Explicit control over shared memory and register tensors (unlike Triton).
• Low-precision types with arbitrary bit-widths (1 to 8 bits).

It also includes automatic tuning, caching, and a Pythonic interface for ease of use.

Tilus is pronounced as tie-lus, /ˈtaɪləs/.

Status

Tilus supports Ampere architecture, and we are actively working on support for Hopper/Blackwell GPUs (see the roadmap).

Getting Started

Installation

Install Tilus using pip:

pip install tilus

Usage

To get started, refer to the tutorials to learn how to program kernels with Tilus.

You can also check more examples of using Tilus.

You can learn more on different topics in the programming guide.

Research

This project is based on the following research paper:

@inproceedings{ding2025tilus,
 author = {Ding, Yaoyao and Hou, Bohan and Zhang, Xiao and Lin, Allan and Chen, Tianqi and Yu, Cody Hao and Wang, Yida and Pekhimenko, Gennady},
 title = {Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation},
 url = {https://doi.org/10.1145/3760250.3762219},
 booktitle = {Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1},
 series = {ASPLOS '26}
}

Acknowledgement

We would like to acknowledge the following projects for their influence on Tilus's design and development:

• Hidet: We take Hidet IR as our low-level target and reuse its runtime system.
• TVM: Hidet's initial IR was adopted from TVM, and we also learned a lot from TVM on how to build a compiler.
• Triton: The core idea of defining kernels at a thread-block level and working with tiles was inspired by Triton.
• Hexcute: We adopted the idea of using automatic layout inference to simplify programming from Hexcute.