37 citations · 155 across the 31 of their papers we have counts for
7 papers · 1 filter
Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations
Ratanond Koonchanok, Michael E. Papka, Khairi Reda
People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has…
Science in a Blink: Supporting Ensemble Perception in Scalar Fields
Victor A. Mateevitsi, Michael E. Papka, Khairi Reda
Visualizations support rapid analysis of scientific datasets, allowing viewers to glean aggregate information (e.g., the mean) within split-seconds. While prior research has explor…
VisAnywhere: Developing Multi-platform Scientific Visualization Applications
Thomas Marrinan, Madeleine Moeller, Alina Kanayinkal +2
Scientists often explore and analyze large-scale scientific simulation data by leveraging two- and three-dimensional visualizations. The data and tasks can be complex and therefore…
MalleTrain: Deep Neural Network Training on Unfillable Supercomputer Nodes
Xiaolong Ma, Feng Yan, Lei Yang +4
First-come first-serve scheduling can result in substantial (up to 10%) of transiently idle nodes on supercomputers. Recognizing that such unfilled nodes are well-suited for deep n…
MRSch: Multi-Resource Scheduling for HPC
Boyang Li, Yuping Fan, Matthew Dearing +4
Emerging workloads in high-performance computing (HPC) are embracing significant changes, such as having diverse resource requirements instead of being CPU-centric. This advancemen…
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
Boyang Li, Zhiling Lan, Michael E. Papka
In the field of high-performance computing (HPC), there has been recent exploration into the use of deep reinforcement learning for cluster scheduling (DRL scheduling), which has d…