Dmitry Duplyakin
Dmitry Duplyakin
Подтвержден адрес электронной почты в домене utah.edu
The Design and Operation of CloudLab
D Duplyakin, R Ricci, A Maricq, G Wong, J Duerig, E Eide, L Stoller, ...
https://www.flux.utah.edu/paper/duplyakin-atc19, 2019
Taming Performance Variability
A Maricq, D Duplyakin, I Jimenez, C Maltzahn, R Stutsman, R Ricci, ...
13th {USENIX} Symposium on Operating Systems Design and Implementation …, 2018
Is big data performance reproducible in modern cloud networks?
A Uta, A Custura, D Duplyakin, I Jimenez, J Rellermeyer, C Maltzahn, ...
17th USENIX symposium on networked systems design and implementation (NSDI …, 2020
Active learning in performance analysis
D Duplyakin, J Brown, R Ricci
2016 IEEE international conference on cluster computing (CLUSTER), 182-191, 2016
Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries
SS SV, JN Law, CE Tripp, D Duplyakin, E Skordilis, D Biagioni, RS Paton, ...
Nature Machine Intelligence 4 (8), 720-730, 2022
Machine and application aware partitioning for adaptive mesh refinement applications
M Fernando, D Duplyakin, H Sundar
Proceedings of the 26th International Symposium on High-Performance Parallel …, 2017
Rebalancing in a multi-cloud environment
D Duplyakin, P Marshall, K Keahey, H Tufo, A Alzabarah
Proceedings of the 4th ACM workshop on Scientific cloud computing, 21-28, 2013
Introducing Configuration Management Capabilities into CloudLab Experiments
D Duplyakin, R Ricci
IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2016
In datacenter performance, the only constant is change
D Duplyakin, A Uta, A Maricq, R Ricci
2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet …, 2020
Workflows community summit: Tightening the integration between computing facilities and scientific workflows
RF da Silva, K Chard, H Casanova, D Laney, D Ahn, S Jha, WE Allcock, ...
arXiv preprint arXiv:2201.07435, 2022
Architecting a persistent and reliable configuration management system
D Duplyakin, M Haney, H Tufo
Proceedings of the 6th Workshop on Scientific Cloud Computing, 11-16, 2015
Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning
D Duplyakin, KF Beckers, DL Siler, MJ Martin, HE Johnston
Energies 15 (3), 967, 2022
Subsurface characterization and machine learning predictions at brady hot springs
KF Beckers, D Duplyakin, MJ Martin, HE Johnston, DL Siler
National Renewable Energy Lab.(NREL), Golden, CO (United States), 2021
On Studying CPU Performance of CloudLab Hardware
D Duplyakin, A Uta, A Maricq, R Ricci
https://www.flux.utah.edu/download?uid=288, 2019
The Part-Time Cloud: Enabling Balanced Elasticity Between Diverse Computing Environments
D Duplyakin, D Johnson, R Ricci
http://www.flux.utah.edu/paper/duplyakin-sciencecloud2017, 2017
Cloud performance variability prediction
Y Zhao, D Duplyakin, R Ricci, A Uta
Companion of the ACM/SPEC International Conference on Performance …, 2021
Bias Characterization, Vertical Interpolation, and Horizontal Interpolation for Distributed Wind Siting Using Mesoscale Wind Resource Estimates
D Duplyakin, S Zisman, C Phillips, H Tinnesand
National Renewable Energy Lab.(NREL), Golden, CO (United States), 2021
Using Machine Learning To Predict Future Temperature Outputs in Geothermal Systems
D Duplyakin, DL Siler, H Johnston, K Beckers, M Martin
National Renewable Energy Lab.(NREL), Golden, CO (United States), 2020
graphenv: a Python library for reinforcement learning on graph search spaces
D Biagioni, CE Tripp, S Clark, D Duplyakin, J Law, PCS John
Journal of Open Source Software 7 (77), 4621, 2022
Validation of wind resource and energy production simulations for small wind turbines in the United States
LM Sheridan, C Phillips, AC Orrell, LK Berg, H Tinnesand, RK Rai, ...
Wind Energy Science 7 (2), 659-676, 2022
В данный момент система не может выполнить эту операцию. Повторите попытку позднее.
Статьи 1–20