Mission of the OpenDOTA project

The mission of OpenDOTA is to establish an Open-Source Data Offloading and Transfer Architecture for modern Data Processing Units (DPUs) that dramatically improves efficiency in high‐performance computing (HPC) and Artificial Intelligence (AI) systems and applications.

By offloading data processing and movement tasks to specialized DPU hardware, OpenDOTA aims to significantly accelerate scientific discovery and innovation, fostering collaboration and advancing state-of-the-art cyberinfrastructure for both academia and industry.

Core System

Develop a high-performance DPU-offloading core that enables ultra-efficient data processing and transfers across HPC/AI infrastructures, eliminating critical bottlenecks and boosting overall throughput.

Tools

Build a suite of configuration and monitoring tools that simplify the adoption of DPU-based data movement and processing, empowering developers to optimize the system and accelerate time to insight.

Benchmarks

Create comprehensive performance benchmarks that measure DPU offloading under diverse workloads, providing transparent metrics to validate improvements and guide system tuning.

Applications

Deliver real-world HPC and AI applications that leverage OpenDOTA for on-the-fly data processing and movement, showcasing practical impact and driving broader adoption in science and industry.

Publications

[HPDC'25] DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge
Arjun Kashyap, Yuke Li, and Xiaoyi Lu
In Proceedings of International ACM Symposium on High Performance and Distributed Computing (HPDC), 2025.
[Paper]
[ICS'25] Understanding the Idiosyncrasies of Emerging BlueField DPUs
Arjun Kashyap, Yuke Li, Darren Ng, and Xiaoyi Lu
In Proceedings of the 39th International Conference on Supercomputing (ICS), 2025.
[Paper]
[IPDPS'24] Accelerating Lossy and Lossless Compression on Emerging BlueField DPU Architectures (Best Paper Award Nomination)
Yuke Li, Arjun Kashyap, Weicong Chen, Yanfei Guo, and Xiaoyi Lu.
In Proceedings of the 38th IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2024.
[Paper]
[IEEE Micro'24] Compression Analysis for BlueField-2/3 Data Processing Units: Lossy and Lossless Perspectives
Yuke Li, Arjun Kashyap, Yanfei Guo, and Xiaoyi Lu
IEEE Micro (Volume: 44, Issue: 2, March-April 2024)
[Paper]
[HotI '23] Characterizing Lossy and Lossless Compression on Emerging BlueField DPU Architectures
Yuke Li, Arjun Kashyap, Yanfei Guo, and Xiaoyi Lu
Proceedings of the 30th IEEE Hot Interconnects Symposium (HotI), 2023
[Paper]
[ModSim '23] Early Experiences in Modeling Performance Implications of DPU-Offloaded Computation
Weicong Chen, Yuke Li, Arjun Kashyap, and Xiaoyi Lu
The 12th annual Workshop on Modeling & Simulation of Systems and Applications (ModSim), 2023
[SC'20] INEC: Fast and Coherent In-Network Erasure Coding
Haiyang Shi and Xiaoyi Lu.
In Proceedings of the 33rd International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2020. (Acceptance Rate: 22.3%)
[Paper]
[SC'19] TriEC: Tripartite Graph Based Erasure Coding NIC Offload (Best Student Paper Finalist)
Haiyang Shi and Xiaoyi Lu.
In Proceedings of the 32nd International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2019. (Acceptance Rate: 22.7%, 78/344)
[Paper]

Interested in Collaboration?

We are too! Contact xiaoyi (dot) lu (at) ucmerced.edu, or click the button below to get more information about the project and how you can get involved.

Acknowledgments

NSF Grants

This work is primarily supported by NSF Research Grant OAC-2505106, with additional support from NSF Award OAC-2340982.

DOE Grant

This work was partially conducted using resources supported by DOE Research Grant DE-SC0024207.

University Support

We sincerely thank the University of California, Merced, for its support.

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