2022 review of data-driven plasma science
Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …
review article highlights the latest development and progress in the interdisciplinary field of …
The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science
We present the Exascale Framework for High Fidelity coupled Simulations (EFFIS), a
workflow and code coupling framework developed as part of the Whole Device Modeling …
workflow and code coupling framework developed as part of the Whole Device Modeling …
Reusability first: Toward FAIR workflows
The FAIR principles of open science (Findable, Accessible, Interoperable, and Reusable)
have had transformative effects on modern large-scale computational science. In particular …
have had transformative effects on modern large-scale computational science. In particular …
Online data analysis and reduction: An important co-design motif for extreme-scale computers
A growing disparity between supercomputer computation speeds and I/O rates means that it
is rapidly becoming infeasible to analyze supercomputer application output only after that …
is rapidly becoming infeasible to analyze supercomputer application output only after that …
A hybrid in situ approach for cost efficient image database generation
The visualization of results while the simulation is running is increasingly common in
extreme scale computing environments. We present a novel approach for in situ generation …
extreme scale computing environments. We present a novel approach for in situ generation …
INSTANT: A Runtime Framework to Orchestrate In-Situ Workflows
In-situ workflow is a type of workflow where multiple components execute concurrently with
data flowing continuously. The adoption of in-situ workflows not only accelerates mission …
data flowing continuously. The adoption of in-situ workflows not only accelerates mission …
Establishing a Generalizable Framework for Generating Cost-Aware Training Data and Building Unique Context-Aware Walltime Prediction Regression Models
S Vallabhajosyula, R Ramnath - … IEEE Intl Conf on Parallel & …, 2022 - ieeexplore.ieee.org
This paper describes a generalizable framework for creating context-aware wall-time
prediction models for HPC applications. This framework:(a) cost-effectively generates …
prediction models for HPC applications. This framework:(a) cost-effectively generates …
Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules
Deep learning models are efficient computational tools that can accelerate the inverse
design of molecules with desired functional properties by generating predictions at a fraction …
design of molecules with desired functional properties by generating predictions at a fraction …
Running Ensemble Workflows at Extreme Scale: Lessons Learned and Path Forward
The ever-increasing volumes of scientific data combined with sophisticated techniques for
extracting information from them have led to the increasing popularity of ensemble …
extracting information from them have led to the increasing popularity of ensemble …
Map applications to target exascale architecture with machine-specific performance analysis, including challenges and projections
This Exascale Computing Project (ECP) milestone report summarizes the status of all 30
ECP Applications Development (AD) subprojects at the end of FY20. In October and …
ECP Applications Development (AD) subprojects at the end of FY20. In October and …