30 citations · 85 across the 12 of their papers we have counts for
7 papers · 1 filter
Integrated Hardware Architecture and Device Placement Search
Irene Wang, Jakub Tarnawski, Amar Phanishayee +1
Distributed execution of deep learning training involves a dynamic interplay between hardware accelerator architecture and device placement strategy. This is the first work to expl…
Forecasting GPU Performance for Deep Learning Training and Inference
Seonho Lee, Amar Phanishayee, Divya Mahajan
Deep learning kernels exhibit predictable memory accesses and compute patterns, making GPUs' parallel architecture well-suited for their execution. Software and runtime systems for…
MGit: A Model Versioning and Management System
Wei Hao, Daniel Mendoza, Rafael da Silva +2
Models derived from other models are extremely common in machine learning (ML) today. For example, transfer learning is used to create task-specific models from "pre-trained" model…
A Study on the Intersection of GPU Utilization and CNN Inference
Jack Kosaian, Amar Phanishayee
There has been significant progress in developing neural network architectures that both achieve high predictive performance and that also achieve high application-level inference…
Efficient Algorithms for Device Placement of DNN Graph Operators
Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur +2
Modern machine learning workloads use large models, with complex structures, that are very expensive to execute. The devices that execute complex models are becoming increasingly h…
The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu +1
Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a signific…