Explanipedia

Stability-preserving Lossy Compression for Large-scale Partial Differential Equations Open

Qian Gong, Jieyang Chen, Xin Liang, Tushar M. Athawale, Qing Liu , et al. · 2025

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment Open

Sairam Sri Vatsavai, R. A. Khan, Kuan-Chieh Hsu, Ozgur O. Kilic, Yihui Ren , et al. · 2025

Error Analysis of Globally Distributed Workflow Management System Open

Sankha Baran Dutta, Ozgur O. Kilic, Tatiana Korchuganova, P. Nilsson, Sairam Sri Vatsavai , et al. · 2025

Data Management System Analysis for Distributed Computing Workloads Open

Kuan-Chieh Hsu, Sairam Sri Vatsavai, Ozgur O. Kilic, Sankha Baran Dutta, Yihui Ren , et al. · 2025

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment Open

Sairam Sri Vatsavai, Raees Khan, Kuan-Chieh Hsu, Ozgur O. Kilic, P. Nilsson , et al. · 2025

Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies. How…

Priority-BF: A Task Manager for Priority-Based Scheduling Open

Ana Gainaru, Scott Klasky, Guillaume Pallez · 2025

CAESAR: A Unified Framework for Foundation and Generative Models for Efficient Compression of Scientific Data Open

Xiao Li, Liangji Zhu, Jaemoon Lee, Rahul Sengupta, Scott Klasky , et al. · 2025

We introduce CAESAR, a new framework for scientific data reduction that stands for Conditional AutoEncoder with Super-resolution for Augmented Reduction. The baseline model, CAESAR-V, is built on a standard variational autoencoder with sca…

HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP Open

Norbert Podhorszki, Greg Eisenhauer, Tahsin Kurç, Qian Gong, Qing Liu , et al. · 2025

Optimising the processing and storage of visibilities using lossy compression Open

Richard Dodson, A. Williamson, Qian Gong, Pascal J. Elahi, A. Wicenec , et al. · 2025

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encounter…

Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of Interest Open

Xuan Wu, Qian Gong, Jieyang Chen, Qing Liu, Norbert Podhorszki , et al. · 2024

The unprecedented amount of scientific data has introduced heavy pressure on the current data storage and transmission systems. Progressive compression has been proposed to mitigate this problem, which offers data access with on-demand pre…

Optimising the Processing and Storage of Visibilities using lossy compression Open

Richard Dodson, A. Williamson, Qian Gong, Pascal Elahi, A. Wicenec , et al. · 2024

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, through increased stations in the array and frequency span. Two primary problems encountered when processing the resultant avalan…

FunM<sup>2</sup>C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices Open

Gautam Hari, Nrushad Joshi, Zhe Wang, Qian Gong, Dave Pugmire , et al. · 2024

Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in v…

A framework for compressing unstructured scientific data via serialization Open

Viktor Reshniak, Qian Gong, Richard Archibald, Scott Klasky, Norbert Podhorszki · 2024

We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preservi…

Streaming Data in HPC Workflows Using ADIOS Open

Greg Eisenhauer, Norbert Podhorszki, Ana Gainaru, Scott Klasky, Philip E. Davis , et al. · 2024

The "IO Wall" problem, in which the gap between computation rate and data access rate grows continuously, poses significant problems to scientific workflows which have traditionally relied upon using the filesystem for intermediate storage…

Enabling High- Throughput Parallel I/O in Particle-in-Cell Monte Carlo Simulations with openPMD and Darshan I/O Monitoring Open

Jeremy J. Williams, Daniel Medeiros, S. Costea, D. Tskhakaya, Franz Poeschel , et al. · 2024

Large-scale HPC simulations of plasma dynamics in fusion devices require efficient parallel I/O to avoid slowing down the simulation and to enable the post-processing of critical information. Such complex simulations lacking parallel I/O c…

A General Framework for Error-controlled Unstructured Scientific Data Compression Open

Qian Gong, Zhe Wang, Viktor Reshniak, Xin Liang, Jieyang Chen , et al. · 2024

Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence in unstructured mesh data, leading to suboptimal compressio…

A Personalized AI Assistant For Intuition-Driven Visual Explorations Open

James L. Hammer, Tanner Hobson, David Pugmire, Scott Klasky, Kenneth Moreland , et al. · 2024

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models Open

Tushar M. Athawale, Zhe Wang, David Pugmire, Kenneth Moreland, Qian Gong , et al. · 2024

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization a…

Lifting MGARD: construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order Open

Viktor Reshniak, Evan Ferguson, Qian Gong, Nicolas Vidal, Richard Archibald , et al. · 2024

MGARD (MultiGrid Adaptive Reduction of Data) is an algorithm for compressing and refactoring scientific data, based on the theory of multigrid methods. The core algorithm is built around stable multilevel decompositions of conforming piece…

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models Open

Tushar M. Athawale, Zhe Wang, David Pugmire, Kenneth Moreland, Qian Gong , et al. · 2024

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization a…

US-UK fusion energy collaborations in the digital space Open

F. I. Parra, Jaydeep Deshpande, B. Dudson, Scott Klasky, Jeffrey C. King , et al. · 2024

The US and UK share the vision for fusion as a vital part of the clean energy future. This vision is reflected in the respective national plans in the form of the Bold Decadal Vision for Commercial Fusion (BDV) in the US and the Spherical …

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

Kshitij Mehta, Massimiliano Lupo Pasini, Stephan Irle, Pilsun Yoo, Frédéric Suter , et al. · 2024

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 of the time required by traditional quantum chemical appr…

Optimizing Metadata Exchange: Leveraging DAOS for ADIOS Metadata I/O Open

Ranjan Sarpangala Venkatesh, Greg Eisenhauer, Norbert Podhorszki, Dmitry Ganyushin, Scott Klasky , et al. · 2024

In HPC I/O middleware like the Adaptable I/O System (ADIOS) often mediates data transfers between applications. The metadata I/O generated by such systems often presents significant scaling and performance limitations. This work seeks impr…

Machine Learning Techniques for Data Reduction of CFD Applications Open

Jaemoon Lee, Ki Sung Jung, Qian Gong, Xiao Li, Scott Klasky , et al. · 2024

We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics (CFD) and other scientific applications. It uses a multidi…

Role of turbulent separatrix tangle in the improvement of the integrated pedestal and heat exhaust issue for stationary-operation tokamak fusion reactors Open

C. S. Chang, S. Ku, R. Hager, Jong Youl Choi, Dave Pugmire , et al. · 2024

The magnetic separatrix surface is designed to provide the final and critical confinement to the hot stationary-operation core plasma in modern tokamak reactors in the absence of an external magnetic perturbation (MP) or transient magneto-…

Hybrid Approaches for Data Reduction of Spatiotemporal Scientific Applications Open

Xiao Li, Qian Gong, Jaemoon Lee, Scott Klasky, Anand Rangarajan , et al. · 2024

Scientists conduct large-scale simulations to compute derived quantities from primary data. Thus, it is crucial that data compression techniques maintain bounded errors on these derived quantities or quantities of interest (QOI). For many …

Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations Open

Dave Pugmire, Jong Youl Choi, Scott Klasky, Ken Moreland, E. Suchyta , et al. · 2024

Understanding the time-varying magnetic field in a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the visualization of magnetic fi…

Fast Algorithms for Scientific Data Compression Open

Tania Banerjee, Jaemoon Lee, Jong Choi, Qian Gong, Jieyang Chen , et al. · 2023

Many scientific simulations and experiments generate terabytes to petabytes of data daily, necessitating data compression techniques. Unlike video and image compression, scientists require methods that accurately preserve primary data (PD)…

MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring Open

Qian Gong, Jieyang Chen, Ben Whitney, Xin Liang, Viktor Reshniak , et al. · 2023

Unraveling Diffusion in Fusion Plasma: A Case Study of In Situ Processing and Particle Sorting Open

Junmin Gu, Paul Lin, Kesheng Wu, S. Ku, C. S. Chang , et al. · 2023

This work starts an in situ processing capability to study a certain diffusion process in magnetic confinement fusion. This diffusion process involves plasma particles that are likely to escape confinement. Such particles carry a significa…

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