A Full-Stack Open-Source High-Performance RDMA Hardware and Software Implementation, Focused on AI Applications, Committed to Surpassing Commercial Solutions
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Ideal for Researchers, University Students, and RDMA Beginners
With the rapid development of AI large model technology, the high-performance networking provided by RDMA has become a critical component of AI infrastructure. However, RDMA technology born in the last century is difficult to fully adapt to today's AI computing scenarios: existing commercial RDMA solutions are constrained by legacy compatibility burdens and struggle to iterate quickly to meet AI demands; RDMA network cards as closed-source black-box products form a significant contradiction with the open-source AI large model software ecosystem. At a time when hardware-software co-optimization is increasingly important, black-box RDMA network cards have become a key bottleneck restricting global optimization of AI systems.
To address these issues, Pazhou Laboratory (Huangpu) and Datanlord jointly initiated the Open-RDMA open-source project. We deeply recognize that relying solely on a single research institution's strength is insufficient to complete the development and debugging of a full-stack system, let alone change the existing industry landscape. Therefore, we chose a full-stack open-source approach, starting from the dimensions of academic research and talent cultivation, leveraging open-source community power to lower the technical barrier of this specialized RDMA field, cultivate RDMA technical talent, enhance the brand influence of the Open-RDMA project community, and gradually expand Open-RDMA's industry influence in the entire RDMA field.
Whether you are a practitioner or student in hardware (FPGA, ASIC), software information technology (driver development, training/inference frameworks, communication protocols), or algorithms (GPU Kernel), you can find a technical direction aligned with your field in the Open-RDMA open-source project.
Break the Black Box, Master the Network | Complete Open-Source RDMA Technology Stack from RTL to Drivers
🌟 Star Us · 📖 Documentation · 🚀 Quick Start · 🤝 Contributing
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From RTL hardware design to Linux user-space drivers, every line of code is transparent and public |
Inspired by RoCE v2 protocol, Ethernet-based with hardware-software co-design for ultra-low latency |
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FPGA-based, freely optimized for AI clusters, GPU communication, and other scenarios—no vendor lock-in |
From verification testing to AI-assisted development tools, accelerating AI infrastructure iteration through AI technology |
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Researchers can quickly customize experimental platforms based on this project to test novel congestion control algorithms and communication protocols |
All materials publicly available with introductory guides, helping more newcomers become RDMA experts |
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Hardware RTL code for the open-rdma project |
Driver code including user-space and kernel-space drivers |
Setting up an environment from hardware simulation to software driver debugging and application development is no easy task, so we provide an out-of-the-box development environment. |
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Language Server for Bluespec SystemVerilog, providing a smooth development experience for BSV projects. |
An AI Agent-driven RTL verification framework launched by Beijing Institute of Open Source Chip, we added support for Cocotb test environment. |
PCIe behavioral and simulation models based on Cocotb, we added Altera RTile support. |
- A Practical Tutorial on PCIe for Total Beginners on Windows
- Although using Windows as an example, most content is platform-agnostic.
- PCIe Deep Dive (Chinese)
We provide a ready-to-use Docker image for new users, containing all development tools and dependencies:
# Pull and start the image
docker run -it open-rdma/dev-env:latest
# Or use docker-compose
docker-compose up -d📖 Detailed Installation Steps
# 1. Clone the repository
git clone https://github.com/open-rdma/open-rdma.git
cd open-rdma
# 2. Install dependencies
make deps
# 3. Build
make build
# 4. Run tests
make testWe welcome all forms of contributions!
- 🐛 Report a Bug
- 💡 Request a Feature
- 📖 Improve documentation
- 🔧 Submit a Pull Request
See Contributing Guide for more details.
- 💬 GitHub Discussions - Questions and discussions
This project is licensed under the Apache 2.0 License.