3 citations · 5 across the 7 of their papers we have counts for
7 papers
Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusters
Eashan Gupta, Yongzhou Chen, Apoorve Mohan +3
Distributed AI training involves recurring rounds of data exchange between multiple pairs of GPU nodes. Slowdown in even one flow due to congestion can cause the entire communicati…
Collective Communication for 100k+ GPUs
Min Si, Pavan Balaji, Yongzhou Chen +36
The increasing scale of large language models (LLMs) necessitates highly efficient collective communication frameworks, particularly as training workloads extend to hundreds of tho…
5G RAN Slicing with Load Balanced Handovers
Yongzhou Chen, Muhammad Taimoor Tariq, Haitham Hassanieh +1
With increasing density of small cells in modern multi-cell deployments, a given user can have multiple options for its serving cell. The serving cell for each user must be careful…
Controlling Congestion via In-Network Content Adaptation
Yongzhou Chen, Ammar Tahir, Radhika Mittal
Realizing that it is inherently difficult to precisely match the sending rates at the endhost with the available capacity on dynamic cellular links, we build a system, Octopus, tha…
FedSS: Federated Learning with Smart Selection of clients
Ammar Tahir, Yongzhou Chen, Prashanti Nilayam
Federated learning provides the ability to learn over heterogeneous user data in a distributed manner while preserving user privacy. However, its current client selection technique…
SLO beyond the Hardware Isolation Limits
Haoran Qiu, Yongzhou Chen, Tianyin Xu +2
Performance isolation is a keystone for SLO guarantees with shared resources in cloud and datacenter environments. To meet SLO requirements, the state of the art relies on hardware…
Skeptical Deep Learning with Distribution Correction
Mingxiao An, Yongzhou Chen, Qi Liu +4
Recently deep neural networks have been successfully used for various classification tasks, especially for problems with massive perfectly labeled training data. However, it is oft…