132 citations
- University of WashingtonUS4 papers
- Assumption CollegeUS3 papers
- Massachusetts Institute of TechnologyUS3 papers
- University of California San DiegoUS3 papers
- University of IowaUS3 papers
- University of LübeckDE3 papers
- University of MichiganUS3 papers
- University of Southern MississippiUS3 papers
- Virginia TechUS3 papers
- Carnegie Mellon UniversityUS2 papers
- Chinese Academy of SciencesCN2 papers
- Fraunhofer Institute for Applied and Integrated SecurityDE2 papers
4 papers · 1 filter
Characterizing Concurrency Mechanisms for NVIDIA GPUs under Deep Learning Workloads
Guin Gilman, Robert J. Walls
We investigate the performance of the concurrency mechanisms available on NVIDIA's new Ampere GPU microarchitecture under deep learning training and inference workloads. In contras…
Enabling Sustainable Clouds: The Case for Virtualizing the Energy System
Noman Bashir, Tian Guo, Mohammad Hajiesmaili +5
Cloud platforms' growing energy demand and carbon emissions are raising concern about their environmental sustainability. The current approach to enabling sustainable clouds focuse…
Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning
Shijian Li, Oren Mangoubi, Lijie Xu +1
Stochastic Gradient Descent (SGD) has become the de facto way to train deep neural networks in distributed clusters. A critical factor in determining the training throughput and mo…
Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers
Shijian Li, Robert J. Walls, Tian Guo
Cloud GPU servers have become the de facto way for deep learning practitioners to train complex models on large-scale datasets. However, it is challenging to determine the appropri…