AHCI RESEARCH GROUP

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2025

Lu, J.; Gao, J.; Feng, F.; He, Z.; Zheng, M.; Liu, K.; He, J.; Liao, B.; Xu, S.; Sun, K.; Mo, Y.; Peng, Q.; Luo, J.; Li, Q.; Lu, G.; Wang, Z.; Dong, J.; He, K.; Cheng, S.; Cao, J.; Jiao, H.; Zhang, P.; Ma, S.; Zhu, L.; Shi, C.; Zhang, Y.; Chen, Y.; Wang, W.; Zhu, S.; Li, X.; Wang, Q.; Liu, J.; Wang, C.; Lin, W.; Zhai, E.; Wu, J.; Liu, Q.; Fu, B.; Cai, D.

Alibaba Stellar: A New Generation RDMA Network for Cloud AI Proceedings Article

In: pp. 453–466, Association for Computing Machinery, Inc, 2025, ISBN: 9798400715242 (ISBN).

Abstract | Links | BibTeX | Tags: Access network, Cloud computing, Congestion control (communication), Containers, data center networking, Data center networkings, Language Model, Learning systems, Machine learning applications, Memory architecture, Network support, Network support for AI and machine learning application, network support for AI and machine learning applications, Performance, Program processors, Remote direct memory access, Stellars, Transport and congestion control, Virtual Reality, Virtualization

@inproceedings{lu_alibaba_2025,

title = {Alibaba Stellar: A New Generation RDMA Network for Cloud AI},

author = {J. Lu and J. Gao and F. Feng and Z. He and M. Zheng and K. Liu and J. He and B. Liao and S. Xu and K. Sun and Y. Mo and Q. Peng and J. Luo and Q. Li and G. Lu and Z. Wang and J. Dong and K. He and S. Cheng and J. Cao and H. Jiao and P. Zhang and S. Ma and L. Zhu and C. Shi and Y. Zhang and Y. Chen and W. Wang and S. Zhu and X. Li and Q. Wang and J. Liu and C. Wang and W. Lin and E. Zhai and J. Wu and Q. Liu and B. Fu and D. Cai},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105016208536&doi=10.1145%2F3718958.3750539&partnerID=40&md5=901fdd27c510072633f0390a0abfb653},

doi = {10.1145/3718958.3750539},

isbn = {9798400715242 (ISBN)},

year  = {2025},

date = {2025-01-01},

pages = {453–466},

publisher = {Association for Computing Machinery, Inc},

abstract = {The rapid adoption of Large Language Models (LLMs) in cloud environments has intensified the demand for high-performance AI training and inference, where Remote Direct Memory Access (RDMA) plays a critical role. However, existing RDMA virtualization solutions, such as Single-Root Input/Output Virtualization (SR-IOV), face significant limitations in scalability, performance, and stability. These issues include lengthy container initialization times, hardware resource constraints, and inefficient traffic steering. To address these challenges, we propose Stellar, a new generation RDMA network for cloud AI. Stellar introduces three key innovations: Para-Virtualized Direct Memory Access (PVDMA) for on-demand memory pinning, extended Memory Translation Table (eMTT) for optimized GPU Direct RDMA (GDR) performance, and RDMA Packet Spray for efficient multi-path utilization. Deployed in our large-scale AI clusters, Stellar spins up virtual devices in seconds, reduces container initialization time by 15 times, and improves LLM training speed by up to 14%. Our evaluations demonstrate that Stellar significantly outperforms existing solutions, offering a scalable, stable, and high-performance RDMA network for cloud AI. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Access network, Cloud computing, Congestion control (communication), Containers, data center networking, Data center networkings, Language Model, Learning systems, Machine learning applications, Memory architecture, Network support, Network support for AI and machine learning application, network support for AI and machine learning applications, Performance, Program processors, Remote direct memory access, Stellars, Transport and congestion control, Virtual Reality, Virtualization},

pubstate = {published},

tppubtype = {inproceedings}

}