1 citations · 1 across the 2 of their papers we have counts for
4 papers
Flint: Compiler Enabled Cluster-Free Design Space Exploration for Distributed ML
Jinsun Yoo, Meghan Cowan, Zheng Du +3
Design space exploration for future distributed Machine Learning systems suffers from a lack of readily available workload representation that enables flexible exploration across t…
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…
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…
Towards a Standardized Representation for Deep Learning Collective Algorithms
Jinsun Yoo, William Won, Meghan Cowan +4
The explosion of machine learning model size has led to its execution on distributed clusters at a very large scale. Many works have tried to optimize the process of producing coll…