1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.DC2026
Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO
Jonas Svedas, Nathan Laubeuf, Ryan Harvey +6
Predicting the performance of large-scale distributed machine learning (ML) workloads across multiple accelerator architectures remains a central challenge in ML system design. Exi…
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…
cs.LG2024
LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation
Mufei Li, Viraj Shitole, Eli Chien +6
Directed acyclic graphs (DAGs) serve as crucial data representations in domains such as hardware synthesis and compiler/program optimization for computing systems. DAG generative m…