97 citations · 625 across the 24 of their papers we have counts for
4 papers · 1 filter
Blink: Fast and Generic Collectives for Distributed ML
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee +3
Model parameter synchronization across GPUs introduces high overheads for data-parallel training at scale. Existing parameter synchronization protocols cannot effectively leverage…
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…
Themis: Fair and Efficient GPU Cluster Scheduling
Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi +4
Modern distributed machine learning (ML) training workloads benefit significantly from leveraging GPUs. However, significant contention ensues when multiple such workloads are run…
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads
Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee +3
With widespread advances in machine learning, a number of large enterprises are beginning to incorporate machine learning models across a number of products. These models are typic…