9 papers
On Topology's Role in ML Training Performance
Sarah McClure, Tegan Wilson, Brad Karp +4
Modern machine learning training workloads run on large-scale networks of compute accelerators. The networks commonly deployed in these systems are typically variations of two basi…
Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems
Shubham Agarwal, Alexander Krentsel, Shu Liu +10
AI agents increasingly excel at generating, testing, and refining code. However, they fall short on tasks requiring formal guarantees of full coverage that testing alone cannot pro…
EnCoR: An end-to-end architecture for simplifying cellular networks
Wesley Woo, Zhuowei Wen, Monniiesh Velmurugan +4
Since their creation, cellular networks have made in-network mobility support a key feature of their service model. While this approach provides seamless connectivity for legacy tr…
Clove: Object-Level CXL Memory Management in Managed Runtimes
Sam Son, Zhihong Luo, Wen Zhang +2
Object-level management of tiered memory has been studied to address the inefficiencies in page-based systems. However, object-level management for CXL-tiered memory remains undere…
GATE: GPU-Accelerated Traffic Engineering for the WAN
Rahul Bothra, Alexander Krentsel, Saptarshi Mandal +4
Traffic engineering (TE) has become a crucial tool for enforcing routing policy and maintaining operational efficiency in large networks. Existing TE solutions pick an objective fu…
Rethinking Network Topologies for Cost-Effective Mixture-of-Experts LLM Serving
Junsun Choi, Sam Son, Sunjin Choi +5
Mixture-of-experts (MoE) architectures have turned LLM serving into a cluster-scale workload in which communication consumes a considerable portion of LLM serving runtime. This has…