2022 review of data-driven plasma science

R Anirudh, R Archibald, MS Asif… - … on Plasma Science, 2023 - ieeexplore.ieee.org
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 …

The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science

E Suchyta, S Klasky, N Podhorszki… - … Journal of High …, 2022 - journals.sagepub.com
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 …

Reusability first: Toward FAIR workflows

M Wolf, J Logan, K Mehta, D Jacobson… - 2021 IEEE …, 2021 - ieeexplore.ieee.org
The FAIR principles of open science (Findable, Accessible, Interoperable, and Reusable)
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

I Foster, M Ainsworth, J Bessac… - … Journal of High …, 2021 - journals.sagepub.com
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 …

A hybrid in situ approach for cost efficient image database generation

V Bruder, M Larsen, T Ertl, H Childs… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …

INSTANT: A Runtime Framework to Orchestrate In-Situ Workflows

F Li, F Song - European Conference on Parallel Processing, 2023 - Springer
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 …

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 …

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules

K Mehta, ML Pasini, S Irle, P Yoo… - 2024 IEEE …, 2024 - ieeexplore.ieee.org
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 …

Running Ensemble Workflows at Extreme Scale: Lessons Learned and Path Forward

K Mehta, A Cliff, F Suter, AM Walker… - 2022 IEEE 18th …, 2022 - ieeexplore.ieee.org
The ever-increasing volumes of scientific data combined with sophisticated techniques for
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

A Siegel, EW Draeger, J Deslippe, T Evans… - 2021 - osti.gov
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 …