activity
20142024
most citedPowerTrain: Fast, Generalizable Time and Power Prediction Models to Optimize DNN Training on Accelerated Edges

9 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE20243 cited

Building AI Agents for Autonomous Clouds: Challenges and Design Principles

Manish Shetty, Yinfang Chen, Gagan Somashekar +10

The rapid growth in the use of Large Language Models (LLMs) and AI Agents as part of software development and deployment is revolutionizing the information technology landscape. Wh…

cs.DC20249 cited

PowerTrain: Fast, Generalizable Time and Power Prediction Models to Optimize DNN Training on Accelerated Edges

Prashanthi S. K., Saisamarth Taluri, Beautlin S +2

Accelerated edge devices, like Nvidia's Jetson with 1000+ CUDA cores, are increasingly used for DNN training and federated learning, rather than just for inferencing workloads. A u…

quant-ph20231 cited

Parallelizing Quantum-Classical Workloads: Profiling the Impact of Splitting Techniques

Tuhin Khare, Ritajit Majumdar, Rajiv Sangle +3

Quantum computers are the next evolution of computing hardware. Quantum devices are being exposed through the same familiar cloud platforms used for classical computers, and enabli…

cs.DC20161 cited

Introducing Distributed Dynamic Data-intensive (D3) Science: Understanding Applications and Infrastructure

Shantenu Jha, Daniel S. Katz, Andre Luckow +3

A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growi…

cs.DC20146 cited

Floe: A Continuous Dataflow Framework for Dynamic Cloud Applications

Yogesh Simmhan, Alok Kumbhare

Applications in cyber-physical systems are increasingly coupled with online instruments to perform long running, continuous data processing. Such "always on" dataflow applications…