21 citations · 97 across the 16 of their papers we have counts for
9 papers · 1 filter
Opportunities of Renewable Energy Powered DNN Inference
Seyed Morteza Nabavinejad, Tian Guo
With the proliferation of the adoption of renewable energy in powering data centers, addressing the challenges of such energy sources has attracted researchers from academia and in…
FuncPipe: A Pipelined Serverless Framework for Fast and Cost-efficient Training of Deep Learning Models
Yunzhuo Liu, Bo Jiang, Tian Guo +4
Training deep learning (DL) models in the cloud has become a norm. With the emergence of serverless computing and its benefits of true pay-as-you-go pricing and scalability, system…
On the Future of Cloud Engineering
David Bermbach, Abhishek Chandra, Chandra Krintz +7
Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many diffe…
Quantifying and Improving Performance of Distributed Deep Learning with Cloud Storage
Nicholas Krichevsky, Renee St Louis, Tian Guo
Cloud computing provides a powerful yet low-cost environment for distributed deep learning workloads. However, training complex deep learning models often requires accessing large…
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…
Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models
Matthew LeMay, Shijian Li, Tian Guo
Deep learning models are increasingly used for end-user applications, supporting both novel features such as facial recognition, and traditional features, e.g. web search. To accom…
Characterizing the Deep Neural Networks Inference Performance of Mobile Applications
Samuel S. Ogden, Tian Guo
Today's mobile applications are increasingly leveraging deep neural networks to provide novel features, such as image and speech recognitions. To use a pre-trained deep neural netw…
CloudCoaster: Transient-aware Bursty Datacenter Workload Scheduling
Samuel S. Ogden, Tian Guo
Today's clusters often have to divide resources among a diverse set of jobs. These jobs are heterogeneous both in execution time and in their rate of arrival. Execution time hetero…