12 citations · 14 across the 5 of their papers we have counts for
5 papers
ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training
Minghao Li, Alicia Golden, Samuel Hsia +14
The rapid scaling of large language model training requires distributing GPU resources across multiple data center buildings and regions. We refer to such paradigm as "scale-across…
Revisiting Reliability in Large-Scale Machine Learning Research Clusters
Apostolos Kokolis, Michael Kuchnik, John Hoffman +7
Reliability is a fundamental challenge in operating large-scale machine learning (ML) infrastructures, particularly as the scale of ML models and training clusters continues to gro…
Generative AI Beyond LLMs: System Implications of Multi-Modal Generation
Alicia Golden, Samuel Hsia, Fei Sun +8
As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal informati…
MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems
Samuel Hsia, Alicia Golden, Bilge Acun +5
Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs. Our ana…
Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging
Zachary DeVito, Michael Mara, Michael Zollhöfer +6
Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes. The mathematical de…