Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution environments in which models inspect repositories, invoke tools, execute commands, and interact with task-specific services. These workloads create sandboxes in large bursts, span heterogeneous functionali...
Jia-Liang Huang, Hong-Xuan Tang, Jing-Chang Chen et al.· 3 citations
Anytest, an in-situ black-box testing tool that localizes root causes of transport-layer NPAs on commodity RoCEv2 RNICs and Ethernet switches without re-cabling or hardware modification, and implements Anytest's DPDK-based endpoints, which realize protocol correctness while enforcing μs-level packet timing at the hardw...
Zhaochen Zhang, Jia-Qi Gao, Sheng Cheng et al.· Conference on Applications,...· 0 citations
Torus networks are deployed in production AI training clusters for their path diversity and low latency, but 2D Torus scales poorly: electrical packet switches compromise latency, and high-dimensional Torus introduces excessive routing complexity. We present STON (Scalable TOrus Network), a hierarchical architecture th...
Qinwei Yang, Peirui Cao, Ruyi Zhang et al.· Fall Joint Computer Conferen...· 0 citations
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