activity
20212026
most citedASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale

78 citations · 160 across the 13 of their papers we have counts for

collaborators

13 papers

cs.DC2026

NUNA: Characterizing and Mitigating Non-Uniform Network Access in Multi-Die GPU Scale-Up Systems

Conor James Green, William Won, Tuan Ta +1

Graphics processing unit (GPU) architectures are growing in size to meet the increasing compute and memory requirements. As GPU sizes increase, intra-socket wire transfer delay inc…

cs.DC2026

ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling

William Won, Jinsun Yoo, Tuan Ta +16

Distributed machine learning (ML) is a key paradigm for today's large-scale artificial intelligence applications. As model inference arises as an important use case, faithful model…

cs.DC2026

PCCL: Process Group-Aware Scalable and Generic Collective Algorithm Synthesizer

William Won, Kartik Lakhotia, Madhu Kumar +2

Distributed machine learning has become increasingly important due to the massive scale of large-scale generative models. Both model parameters and data are distributed across many…

cs.LG2026

How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving

Hanjiang Wu, Abhimanyu Rajeshkumar Bambhaniya, Sarbartha Banerjee +9

Modern large language model (LLM) inference has progressively disaggregated to keep pace with growing model sizes and tight TTFT and TPOT service-level objectives: from chunked-pre…

cs.DC2026

MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces

Srinivas Sridharan, Theodor-Adrian Badea, Andy Balogh +26

The fast pace of artificial intelligence~(AI) innovation demands an agile methodology for observation, reproduction and optimization of distributed machine learning~(ML) workload b…

cs.DC2025★ 1 cited

COSMIC: Enabling Full-Stack Co-Design and Optimization of Distributed Machine Learning Systems

Aditi Raju, Jared Ni, William Won +6

Large-scale machine learning models necessitate distributed systems, posing significant design challenges due to the large parameter space across distinct design stacks. Existing s…