Publications

Showing 77 results for Author: Jong Youl Choi

View All publications

  • Jun, 2026

    Conference Paper

    Multi-task Parallelism for Robust Pre-training of Graph Foundation Models on Multi-source, Multi-fidelity Atomistic Modeling Data

    Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during pre-training, recent studies employ multi-task learning, in which shared message passing layers initially process input atomistic structures regardless of source, then route them to multiple decod…

  • Jun, 2026

    Journal

    Scalable Hybrid Learning Techniques for Scientific Data Compression

    Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit errors to primary data (PD), scientists require compression techniques that accurately preserve derived quantities of interest…

  • Jun, 2026

    ORNL Report

    User Manual – HydraGNN v5.0: Distributed Implementation of Multi-Tasking Graph Neural Networks

    This document serves as the user manual for HydraGNN v5.0, a scalable graph neural network (GNN) architecture for simultaneous prediction of multiple target properties using multi-task learning (MTL). This version of HydraGNN has been developed primarily to support the development, training, and deployment of predictive graph-based deep learning (DL) models for atomistic m…

  • Mar, 2026

    Conference Paper

    ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling

    Sparse observations and coarse-resolution climate models limit effective regional decision-making, underscoring the need for robust downscaling. However, existing AI methods struggle with generalization across variables and geographies and are constrained by the quadratic complexity of Vision Transformer (ViT) self-attention. We introduce ORBIT-2, a scalable foundation mod…

  • Mar, 2026

    Conference Paper

    Analyzing File Access Patterns on Large-Scale HPC Systems: Opportunities for File Prefetching

    This paper explores the potential opportunities for implementing file prefetching techniques on large-scale high-performance computing (HPC) systems. Specifically, we investigate the file access patterns of various applications across multiple scientific domains using two years' worth of Darshan I/O traces obtained from the Summit supercomputer. We identify recurring trend…

  • Mar, 2026

    Conference Paper

    Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling

    Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity t…

  • Mar, 2026

    Conference Paper

    Fast Algorithms for Scientific Data Compression

    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) and derived quantities of interest (QoIs). In our previous work, we demonstrated the effectiveness of hybrid compression techniques tha…

  • Mar, 2026

    Conference Paper

    Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training

    With the end of Moore’s law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intelligent subsampling? To explore this, we develop SICKLE, a sparse intelligent curation framework for efficient learning, featuring a novel maximum entropy (MaxEnt) sampling approach, scalable train…

  • Mar, 2026

    Conference Paper

    Sequence length scaling in vision transformers for scientific images on frontier

    Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence lengths. While transformers for text have inspired scaling sequence lengths in ViTs, adapting these for ViTs introduces unique challenges. We develop distributed sequence parallelism for ViTs, enabling…

  • Mar, 2026

    Conference Paper

    MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training

    Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance dependin…

  • Mar, 2026

    Conference Paper

    Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations

    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 fields in fusion devices. However, Poincaré plots can be very computationally expensive making it impractical, for example, to generate th…

1
2
…
7