63 citations · 98 across the 8 of their papers we have counts for
8 papers
D-STACK: High Throughput DNN Inference by Effective Multiplexing and Spatio-Temporal Scheduling of GPUs
Aditya Dhakal, Sameer G. Kulkarni, K. K. Ramakrishnan
Hardware accelerators such as GPUs are required for real-time, low-latency inference with Deep Neural Networks (DNN). However, due to the inherent limits to the parallelism they ca…
Analyzing Open-Source Serverless Platforms: Characteristics and Performance
Junfeng Li, Sameer G. Kulkarni, K. K. Ramakrishnan +1
Serverless computing is increasingly popular because of its lower cost and easier deployment. Several cloud service providers (CSPs) offer serverless computing on their public clou…
Spatial Sharing of GPU for Autotuning DNN models
Aditya Dhakal, Junguk Cho, Sameer G. Kulkarni +2
GPUs are used for training, inference, and tuning the machine learning models. However, Deep Neural Network (DNN) vary widely in their ability to exploit the full power of high-per…
CoShare: An Efficient Approach for Redundancy Allocation in NFV
Yordanos Tibebu Woldeyohannes, Besmir Tola, Yuming Jiang +1
An appealing feature of Network Function Virtualization (NFV) is that in an NFV-based network, a network function (NF) instance may be placed at any node. On the one hand this offe…
Understanding Open Source Serverless Platforms: Design Considerations and Performance
Junfeng Li, Sameer G. Kulkarni, K. K. Ramakrishnan +1
Serverless computing is increasingly popular because of the promise of lower cost and the convenience it provides to users who do not need to focus on server management. This has r…
SDNFV: Flexible and Dynamic Software Defined Control of an Application- and Flow-Aware Data Plane
Wei Zhang, Guyue Liu, Timothy Wood +2
Software Defined Networking (SDN) promises greater flexibility for directing packet flows, and Network Function Virtualization promises to enable dynamic management of software-bas…