A5054451484- Advanced Data Storage Technologies
- Distributed and Parallel Computing Systems
- Scientific Computing and Data Management
- Magnetic confinement fusion research
- Parallel Computing and Optimization Techniques
- Machine Learning in Materials Science
- Superconducting Materials and Applications
- Data Visualization and Analytics
- Algorithms and Data Compression
- Ionosphere and magnetosphere dynamics
- Advanced Graph Neural Networks
- Advanced Database Systems and Queries
- Particle accelerators and beam dynamics
- Computational Drug Discovery Methods
- Structural Load-Bearing Analysis
- Peer-to-Peer Network Technologies
- Intermetallics and Advanced Alloy Properties
- Time Series Analysis and Forecasting
- Nuclear Materials and Properties
- Electron and X-Ray Spectroscopy Techniques
- Internet Traffic Analysis and Secure E-voting
- Gene expression and cancer classification
- Metal and Thin Film Mechanics
- Network Security and Intrusion Detection
- Advanced Image and Video Retrieval Techniques
Oak Ridge National Laboratory
2016-2025
Seoul Women's University
2021
Korea Institute of Fusion Energy
2020
Hanbat National University
2019
Indiana University
2006-2012
Indiana University Bloomington
2005-2011
NuVasive (United States)
2010
We present ADIOS 2, the latest version of Adaptable Input Output (I/O) System. 2 addresses scientific data management needs ranging from scalable I/O in supercomputers, to analysis personal computer and cloud systems. Version introduces a unified application programming interface (API) that enables seamless movement through files, wide-area-networks, direct memory access, as well high-level APIs for analysis. The internal architecture provides set reusable extendable components managing...
Developing an automated active learning framework for Neural Network Potentials, focusing on accurately simulating bond-breaking in hexane chains through steered molecular dynamics sampling and assessing model transferability.
We investigate how to obtain a balance between privacy and audit requirements in vehicular networks. Challenging the current trend of relying on asymmetric primitives within VANETs, our investigation is feasibility study use symmetric primitives, resulting some efficiency improvements potential value. More specifically, we develop realistic trust model, an architecture that supports solution. In order ascertain most users will not find it meaningful disconnect or disable transponders, design...
Scientific simulations generate large amounts of floating-point data, which are often not very compressible using the traditional reduction schemes, such as deduplication or lossless compression. The emergence lossy compression holds promise to satisfy data demand from HPC applications; however, has been widely adopted in science production. We believe a fundamental reason is that there lack understanding benefits, pitfalls, and performance on scientific data. In this paper, we conduct...
Clouds and MapReduce have shown themselves to be a broadly useful approach scientific computing especially for parallel data intensive applications. However they limited applicability some areas such as mining because has poor performance on problems with an iterative structure present in the linear algebra that underlies much analysis. Such can run efficiently clusters using MPI leading hybrid cloud cluster environment. This motivates design implementation of open source Iterative system...
We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic structural properties refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. start by training GNN model only DFT that describes binary niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), tantalum-vanadium (Ta-V) predict formation enthalpy root mean squared displacement. Once trained, are...
In biology,a vaccine is a weakened strain of virus or bacterium that intentionally injected into the body for purpose stimulating antibody production.Inspired by this idea, we propose packet mechanism randomizes address-like strings in payloads to carry out fast exploit detection, vulnerability diagnosis and signature generation. An with randomized jump address behaves like vaccine: it will likely cause an exception vulnerable program's process when attempting hijack control flow,and thereby...
The recent explosion of publicly available biology gene sequences and chemical compounds offers an unprecedented opportunity for data mining. To make analysis feasible such vast volume high-dimensional scientific data, we apply high performance dimension reduction algorithms. It facilitates the investigation unknown structures in a three dimensional visualization. Among known algorithms, utilize multidimensional scaling generative topographic mapping algorithms to configure given into target...
SUMMARY Cloud computing offers exciting new approaches for scientific that leverage major commercial players’ hardware and software investments in large‐scale data centers. Loosely coupled problems are very important many fields, with the ongoing move towards data‐intensive computing, they on rise. There exist several different to leveraging clouds cloud‐oriented processing frameworks perform pleasingly parallel (also called embarrassingly parallel) computations. In this paper, we present...
A ground source heat pump (GSHP) system has higher performance than air due to the use of more efficient source. However, GSHP depends on thermal properties and groundwater conditions. There are many studies improvement by developing exchanger (GHX) exchange method. Several have suggested methods improve rate for development GHX. few real-scale experimental quantitatively analyzed their using same Therefore, objective this study was evaluate various pipe types GHX response test (TRT) under...
We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties demonstrate with ferromagnetic materials. train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) fixed body centered tetragonal (BCT) lattice structure volume the mixing enthalpy (a feature of system), charge transfer, magnetic moment across configurations that span entire compositional range. By...
With the growing computational complexity of science and new emerging hardware, it is time to re-evaluate traditional monolithic design codes. One paradigm constructing larger scientific experiments from coupling multiple individual applications, each targeting their own physics, characteristic lengths, and/or scales. We present a framework constructed by leveraging capabilities such as in-memory communications, workflow scheduling on HPC resources, continuous performance monitoring. This...
We present the Exascale Framework for High Fidelity coupled Simulations (EFFIS), a workflow and code coupling framework developed as part of Whole Device Modeling Application (WDMApp) in Computing Project. EFFIS consists library, command line utilities, collection run-time daemons. Together, these software products enable users to easily compose execute workflows that include: strong or weak coupling, situ (or offline) analysis/visualization/monitoring, command-and-control actions, remote...
In this position paper, we argue that the loosely coupled in situ processing paradigm will play an important role high performance computing for foreseeable future. Loosely is enabling technique addresses many of current issues with tightly situ, including, ease-of-integration, usability, and fault tolerance. We survey prominent positives negatives both present our recommendation as to why here stay. then report on some recent experiences processing, effort explore each discussed factors a...
We present a scheme that spatially couples two gyrokinetic codes using first-principles. Coupled equations are presented and necessary sufficient condition for ensuring accuracy is derived. This new both the field particle distribution function. The coupling of function only performed once every few time-steps, five-dimensional (5D) grid to communicate between codes. 5D interface enables different types models, such as continuum codes, or delta-f total-f models. Transferring information from...
Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict properties from the graph representation molecular structures. Training an accurate and comprehensive GCNN surrogate for design requires large-scale datasets usually time-consuming process. Recent advances GPUs distributed computing open path reduce computational cost training effectively. However, efficient utilization high performance (HPC) resources...
Microarray technology is a high-throughput experimental technique that can measure expression levels of hundreds thousands genes simultaneously. To interpret massive data from gene-expression microarray experiments, biologists encounter computational and analytical challenges. This especially challenging for small research labs lack local computing bioinformatics expertise. Here, we introduce virtual analysis system gene in clouds with flexible configurable GUI workflow engine so are able to...
Large high dimension datasets are of growing importance in many fields and it is important to be able visualize them for understanding the results data mining approaches or just browsing a way that distance between points visualization (2D 3D) space tracks original dimensional space. Dimension reduction well understood approach but can very time memory intensive large problems. Here we report on parallel algorithms Scaling by MAjorizing Complicated Function (SMACOF) solve Multidimensional...