# KDA: Kernel Design Agents for High-Performance CUDA Kernel Development

This repository profile is provided by osrepos.com, an open source repository discovery platform.

Source: osrepos.com
Repository profile: https://osrepos.com/repo/nvlabs-kda
Generated for open source discovery and AI-assisted research.

Kernel Design Agents (KDA) offers an agent-centric workflow designed to streamline the research, implementation, verification, and iteration of performance-sensitive CUDA kernel tasks. This innovative approach leverages coding agents to accelerate the development of high-performance kernels. It is an early research prototype from NVlabs, welcoming community feedback and contributions.

GitHub: https://github.com/NVlabs/kda
OSRepos URL: https://osrepos.com/repo/nvlabs-kda

## Summary

Kernel Design Agents (KDA) offers an agent-centric workflow designed to streamline the research, implementation, verification, and iteration of performance-sensitive CUDA kernel tasks. This innovative approach leverages coding agents to accelerate the development of high-performance kernels. It is an early research prototype from NVlabs, welcoming community feedback and contributions.

## Topics

- CUDA
- AI Agents
- High Performance Computing
- GPU Programming
- Kernel Optimization
- NVlabs
- Workflow Automation

## Repository Information

Last analyzed by OSRepos: Sat Sep 26 2026 09:25:23 GMT+0100 (Western European Summer Time)
Detail views: 0
GitHub clicks: 0

## Safety Notice

OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything.

## Content

## Introduction

Kernel Design Agents (KDA) is an agent-centric workflow developed by NVlabs, designed to leverage coding agents for researching, implementing, verifying, and iterating on performance-sensitive CUDA kernel tasks. This repository presents an early research prototype that is under active development, welcoming community feedback and contributions to further its evolution.

## Why Use KDA and Its Benefits

KDA offers a structured and agent-driven approach to a traditionally complex area: high-performance CUDA kernel development. Its key benefits include:

*   **Agent-Centric Workflow**: Automates and streamlines various stages of kernel development, from initial research to final verification, using intelligent coding agents.
*   **Performance Focus**: Specifically tailored for tasks where performance is critical, helping developers achieve optimized CUDA kernels.
*   **Community Kernel Wishlist**: Provides a unique platform for users to submit requests for kernel optimizations, currently supporting NVIDIA B200 and B300 GPUs, fostering collaborative improvement.
*   **Flexible and Independent**: The workflow is designed to be independent of any single benchmark harness or hardware target, allowing for broad applicability.
*   **Structured Development**: Promotes a recommended workspace layout that ensures comprehensive documentation of the agent's work, including drafts, plans, runs, outputs, and benchmarks, making the development process transparent and reproducible.

## Installation

To get started with Kernel Design Agents, follow these steps to clone the repository and set up the necessary skills:

bash
git clone --recurse-submodules https://github.com/mit-han-lab/kernel-design-agents.git
cd kernel-design-agents

# Link skills
mkdir -p ~/.claude/skills
ln -s "$(pwd)/skills/ncu-report-skill" ~/.claude/skills/ncu-report-skill
ln -s "$(pwd)/skills/KernelWiki" ~/.claude/skills/KernelWiki


Alternatively, you can clone the independently licensed `ncu-report-skill` directly:

bash
mkdir -p ~/.claude/skills && cd ~/.claude/skills
git clone https://github.com/mit-han-lab/ncu-report-skill.git


Additionally, install the `humanize` Claude Code plugin from the Claude Code plugin UI:

text
/plugin marketplace add PolyArch/humanize
/plugin install humanize@PolyArch


## Examples

KDA provides a clear "Minimal Flow" and a "Recommended Workspace Layout" to guide users in leveraging the agent-centric approach effectively.

### Minimal Flow

1.  Create a separate implementation workspace for your target task.
2.  Define the task contract: objective, constraints, validation command, and promotion criteria.
3.  Start an agent session in the implementation workspace.
4.  Provide the agent with `prompts/basic-flow.md`, filled with task-specific details.
5.  Ask the agent to draft a short plan to `docs/draft.md` in the implementation workspace.
6.  Convert the draft into an executable plan, either manually or using a planning tool like Humanize.
7.  Implement in small iterations, verifying after each meaningful change.
8.  Record candidates, benchmark or evaluation results, profiling evidence, and final promotion decisions.

### Recommended Workspace Layout

For optimal organization and reproducibility, it is recommended to use a workspace layout similar to this, keeping implementation work separate from the KDA reference material:

text
task-workspace/
  docs/
    draft.md
    plan.md
  runs/
  outputs/
  profile/
  benchmark.csv
  candidates.jsonl


This layout ensures that enough context is recorded for another engineer to understand the process, validated outcomes, and the rationale behind the final candidate selection.

## Links

*   **GitHub Repository**: [https://github.com/NVlabs/kda](https://github.com/NVlabs/kda)
*   **Project Website**: [https://nvlabs.github.io/kda/](https://nvlabs.github.io/kda/)
*   **Contributing Guide**: [https://github.com/NVlabs/kda/blob/main/CONTRIBUTING.md](https://github.com/NVlabs/kda/blob/main/CONTRIBUTING.md)