Massively parallel Wang-Landau sampling on multiple GPUs. - Junqi Yin; D. P. Landau (Comput. Phys. Commun., 2012)

Instrumenting Genomic Sequence Analysis Pipeline Mothur on Shared Memory Architecture. - Junqi Yin; Bhanu Rekepalli; Pragneshkumar B. Patel; Chanda Drennen; Annette Summers Engel (XSEDE, 2014)

fMRI image registration with AFNI's 3dQwarp. - Junqi Yin; R. Glenn Brook; Lonnie D. Crosby; Mitchel D. Horton; Chad Burdyshaw; Frank Skidmore; Jon Marstrander; Thomas Anthony 002; Yuliang Liu (BCB, 2015)

Integrating apache spark into PBS-Based HPC environments. - Troy Baer; Paul Peltz Jr.; Junqi Yin; Edmon Begoli (XSEDE, 2015)

Performance assessment of real-time estimation of continuous-time stochastic volatility of financial data on GPUs. - Daniel Krulewich; Junqi Yin; Brent H. Bundick; Yong Zeng (XSEDE, 2015)

Analysis on spatio-temporal trends and drivers in vegetation growth during recent decades in Xinjiang, China. - Jiaqiang Du; Jianmin Shu; Junqi Yin; Xinjie Yuan; Jiaerheng Ahati; Shanshan Xiong; Ping He; Weiling Liu (Int. J. Appl. Earth Obs. Geoinformation, 2015)

Optimization of non-linear image registration in AFNI. - Junqi Yin; Thomas Anthony 002; Jon Marstrander; Yuliang Liu; Chad Burdyshaw; Mitchel D. Horton; Lonnie D. Crosby; R. Glenn Brook; Frank Skidmore (XSEDE, 2016)

Autumn NDVI contributes more and more to vegetation improvement in the growing season across the Tibetan Plateau. - Jiaqiang Du; Ping He; Shifeng Fang; Weiling Liu; Xinjie Yuan; Junqi Yin (Int. J. Digit. Earth, 2017)

The design, deployment, and evaluation of the CORAL pre-exascale systems. - Sudharshan S. Vazhkudai; Bronis R. de Supinski; Arthur S. Bland; Al Geist; James C. Sexton; Jim Kahle; Christopher Zimmer 001; Scott Atchley; Sarp Oral; Don E. Maxwell; Verónica G. Vergara Larrea; Adam Bertsch; et al. (SC, 2018)

Defining Big Data Analytics Benchmarks for Next Generation Supercomputers. - Drew Schmidt; Junqi Yin; Michael A. Matheson; Bronson Messer; Mallikarjun Shankar (CoRR, 2018)

Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer. - Junqi Yin; Shubhankar Gahlot; Nouamane Laanait; Ketan Maheshwari; Jack Morrison; Sajal Dash; Mallikarjun Shankar (DLS@SC, 2019)

A greedy constructive algorithm for the optimization of neural network architectures. - Massimiliano Lupo Pasini; Junqi Yin; Ying Wai Li; Markus Eisenbach 002 (CoRR, 2019)

Exascale Deep Learning for Scientific Inverse Problems. - Nouamane Laanait; Joshua Romero; Junqi Yin; M. Todd Young; Sean Treichler; Vitalii Starchenko; Albina Y. Borisevich; Alex Sergeev; Michael A. Matheson (CoRR, 2019)

Integrating Deep Learning in Domain Sciences at Exascale. - Rick Archibald; Edmond Chow; Eduardo F. D'Azevedo; Jack J. Dongarra; Markus Eisenbach 002; Rocco Febbo; Florent Lopez; Daniel Nichols; Stanimire Tomov; Kwai Wong; Junqi Yin (SMC, 2020)

Performance Evaluation of Python Based Data Analytics Frameworks in Summit: Early Experiences. - Benjamín Hernández; Suhas Somnath; Junqi Yin; Hao Lu 001; Joe Eaton; Peter Entschev; John Kirkham; Zahra Ronaghi (SMC, 2020)

Toward Real-Time Analysis of Synchrotron Micro-Tomography Data: Accelerating Experimental Workflows with AI and HPC. - James E. McClure; Junqi Yin; Ryan T. Armstrong; Ketan C. Maheshwari; Sean R. Wilkinson; Lucas Vlcek; Ying Da Wang; Mark A. Berrill; Mark Rivers (SMC, 2020)

Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics. - Suzanne Parete-Koon; Peter F. Peterson; Garrett E. Granroth; Wenduo Zhou; Pravallika Devineni; Nouamane Laanait; Junqi Yin; Albina Y. Borisevich; Ketan Maheshwari; Melissa R. Allen-Dumas; Srinath Ravulaparthy; Kuldeep R. Kurte; et al. (SMC, 2020)

Distributed Training and Optimization Of Neural Networks. - Jean-Roch Vlimant; Junqi Yin (CoRR, 2020)

Data optimization for large batch distributed training of deep neural networks. - Shubhankar Gahlot; Junqi Yin; Mallikarjun Shankar (CoRR, 2020)

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. - Atanu Acharya; Rupesh Agarwal; Matthew B. Baker; Jérôme Baudry; Debsindhu Bhowmik; Swen Böhm; Kendall G. Byler; Sam Yen-Chi Chen; Leighton Coates; Connor J. Cooper; Omar Demerdash; Isabella Daidone; et al. (J. Chem. Inf. Model., 2020)

Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening. - M. P. Oxley; Junqi Yin; N. Borodinov; Suhas Somnath; Maxim A. Ziatdinov; Andrew R. Lupini; Stephen Jesse; Rama K. Vasudevan; Sergei V. Kalinin (Mach. Learn. Sci. Technol., 2020)

Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence. - Massimiliano Lupo Pasini; Junqi Yin (CSCI, 2021)

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads. - Aymen Al Saadi; Dario Alfè; Yadu N. Babuji; Agastya Bhati; Ben Blaiszik; Alexander Brace; Thomas S. Brettin; Kyle Chard; Ryan Chard; Austin Clyde; Peter V. Coveney; Ian T. Foster; et al. (ICPP, 2021)

Production Deployment of Machine-Learned Rotorcraft Surrogate Models on HPC. - Wesley Brewer; Daniel Martínez; Mathew Boyer; Dylan Jude; Andy Wissink; Ben Parsons; Junqi Yin; Valentine Anantharaj (MLHPC@SC, 2021)

MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (MLHPC@SC, 2021)

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary. - Sajal Dash; Junqi Yin; Mallikarjun Shankar; Feiyi Wang; Wu-chun Feng (DRBSD@SC, 2021)

MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (CoRR, 2021)

Stable Anderson Acceleration for Deep Learning. - Massimiliano Lupo Pasini; Junqi Yin; Viktor Reshniak; Miroslav Stoyanov (CoRR, 2021)

Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites. - Eunice Cho; Margarida Rosa; Ruhi Anjum; Saman Mehmood; Mariya Soban; Moniza Mujtaba; Khair Bux; Syed Tarique Moin; Mohammad Tanweer; Sarath Dantu; Alessandro Pandini; Junqi Yin; et al. (J. Chem. Inf. Model., 2021)

Neural network-based order parameter for phase transitions and its applications in high-entropy alloys. - Junqi Yin; Zongrui Pei; Michael C. Gao (Nat. Comput. Sci., 2021)

A scalable algorithm for the optimization of neural network architectures. - Massimiliano Lupo Pasini; Junqi Yin; Ying Wai Li; Markus Eisenbach 002 (Parallel Comput., 2021)

Scalable balanced training of conditional generative adversarial neural networks on image data. - Massimiliano Lupo Pasini; Vittorio Gabbi; Junqi Yin; Simona Perotto; Nouamane Laanait (J. Supercomput., 2021)

Learning to Scale the Summit: AI for Science on a Leadership Supercomputer. - Wayne Joubert; Bronson Messer; Philip C. Roth; Antigoni Georgiadou; Justin Lietz; Markus Eisenbach 002; Junqi Yin (IPDPS Workshops, 2022)

Strategies for Integrating Deep Learning Surrogate Models with HPC Simulation Applications. - Junqi Yin; Feiyi Wang; Mallikarjun Shankar (IPDPS Workshops, 2022)

Accelerating Collective Communication in Data Parallel Training across Deep Learning Frameworks. - Joshua Romero; Junqi Yin; Nouamane Laanait; Bing Xie; M. Todd Young; Sean Treichler; Vitalii Starchenko; Albina Y. Borisevich; Alex Sergeev; Michael A. Matheson (NSDI, 2022)

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials. - Markus Eisenbach 002; Mariia Karabin; Massimiliano Lupo Pasini; Junqi Yin (SMC, 2022)

Toward an Autonomous Workflow for Single Crystal Neutron Diffraction. - Junqi Yin; Guannan Zhang; Huibo Cao; Sajal Dash; Bryan C. Chakoumakos; Feiyi Wang (SMC, 2022)

AI Benchmarking for Science: Efforts from the MLCommons Science Working Group. - Jeyan Thiyagalingam; Gregor von Laszewski; Junqi Yin; Murali Emani; Juri Papay; Gregg Barrett; Piotr Luszczek; Aristeidis Tsaris; Christine R. Kirkpatrick; Feiyi Wang; Tom Gibbs; Venkatram Vishwanath; et al. (ISC Workshops, 2022)

Language models for the prediction of SARS-CoV-2 inhibitors. - Andrew E. Blanchard; John Gounley; Debsindhu Bhowmik; Mayanka Chandra Shekar; Isaac Lyngaas; Shang Gao 008; Junqi Yin; Aristeidis Tsaris; Feiyi Wang; Jens Glaser (Int. J. High Perform. Comput. Appl., 2022)

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action. - Anda Trifan; Defne Gorgun; Michael Salim; Zongyi Li; Alexander Brace; Maxim Zvyagin; Heng Ma; Austin Clyde; David Clark; David J. Hardy; Tom Burnley; Lei Huang 019; et al. (Int. J. High Perform. Comput. Appl., 2022)

Atomic structure generation from reconstructing structural fingerprints. - Victor Fung; Shuyi Jia; Jiaxin Zhang 005; Sirui Bi; Junqi Yin; Panchapakesan Ganesh (Mach. Learn. Sci. Technol., 2022)

Distributing Simplex-Shaped Nested for-Loops to Identify Carcinogenic Gene Combinations. - Sajal Dash; Mohammad Alaul Haque Monil; Junqi Yin; Ramu Anandakrishnan; Feiyi Wang (IPDPS, 2023)

DeepThermo: Deep Learning Accelerated Parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys. - Junqi Yin; Feiyi Wang; Mallikarjun Arjun Shankar (IPDPS, 2023)

FORGE: Pre-Training Open Foundation Models for Science. - Junqi Yin; Sajal Dash; Feiyi Wang; Mallikarjun Shankar (SC, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

Optimizing Distributed Training on Frontier for Large Language Models. - Sajal Dash; Isaac Lyngaas; Junqi Yin; Xiao Wang 004; Romain Egele; Guojing Cong; Feiyi Wang; Prasanna Balaprakash (CoRR, 2023)

Stable parallel training of Wasserstein conditional generative adversarial neural networks. - Massimiliano Lupo Pasini; Junqi Yin (J. Supercomput., 2023)

Evaluation of pre-training large language models on leadership-class supercomputers. - Junqi Yin; Sajal Dash; John Gounley; Feiyi Wang; Georgia D. Tourassi (J. Supercomput., 2023)

The Case for Co-Designing Model Architectures with Hardware. - Quentin Anthony; Jacob Hatef; Deepak Narayanan; Stella Biderman; Stas Bekman; Junqi Yin; Aamir Shafi; Hari Subramoni; Dhabaleswar K. Panda 001 (ICPP, 2024)

Pretraining Billion-Scale Geospatial Foundational Models on Frontier. - Aristeidis Tsaris; Philipe Ambrozio Dias; Abhishek Potnis; Junqi Yin; Feiyi Wang; Dalton D. Lunga (IPDPS (Workshops), 2024)

Comparative Study of Large Language Model Architectures on Frontier. - Junqi Yin; Avishek Bose; Guojing Cong; Isaac Lyngaas; Quentin Anthony (IPDPS, 2024)

SciTrust: Evaluating the Trustworthiness of Large Language Models for Science. - Emily Herron; Junqi Yin; Feiyi Wang (SC Workshops, 2024)

Enabling Low-Overhead HT-HPC Workflows at Extreme Scale using GNU Parallel. - Ketan Maheshwari; William Arndt; Ahmad Maroof Karimi; Junqi Yin; Frédéric Suter; Seth Johnson; Rafael Ferreira da Silva (SC Workshops, 2024)

A Scalable Training-Free Diffusion Model for Uncertainty Quantification. - Ali Haisam Muhammad Rafid; Junqi Yin; Yuwei Geng; Siming Liang; Feng Bao 005; Lili Ju; Guannan Zhang (SC Workshops, 2024)

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability. - Xiao Wang 004; Siyan Liu; Aristeidis Tsaris; Jong-Youl Choi; Ashwin M. Aji; Ming Fan; Wei Zhang 261; Junqi Yin; Moetasim Ashfaq; Dan Lu 001; Prasanna Balaprakash (SC, 2024)

A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics. - Junqi Yin; Siming Liang; Siyan Liu; Feng Bao 005; Hristo G. Chipilski; Dan Lu 001; Guannan Zhang (SC Workshops, 2024)

Optimizing Distributed Training on Frontier for Large Language Models. - Sajal Dash; Isaac Lyngaas; Junqi Yin; Xiao Wang 004; Romain Egele; J. Austin Ellis; Matthias Maiterth; Guojing Cong; Feiyi Wang; Prasanna Balaprakash (ISC, 2024)

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability. - Xiao Wang 004; Aristeidis Tsaris; Siyan Liu; Jong-Youl Choi; Ming Fan; Wei Zhang 261; Junqi Yin; Moetasim Ashfaq; Dan Lu 001; Prasanna Balaprakash (CoRR, 2024)

Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier. - Aristeidis Tsaris; Chengming Zhang 006; Xiao Wang 004; Junqi Yin; Siyan Liu; Moetasim Ashfaq; Ming Fan; Jong-Youl Choi; Mohamed Wahib; Dan Lu 001; Prasanna Balaprakash; Fei-Yue Wang 001 (CoRR, 2024)

Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars. - Wesley Brewer; Aditya Kashi; Sajal Dash; Aristeidis Tsaris; Junqi Yin; Mallikarjun Shankar; Feiyi Wang (CoRR, 2024)

Editorial: Big scientific data analytics on HPC and cloud. - Jianwu Wang; Junqi Yin; Mai H. Nguyen; Jingbo Wang; Weijia Xu (Frontiers Big Data, 2024)

chatHPC: Empowering HPC users with large language models. - Junqi Yin; Jesse Hines; Emily Herron; Tirthankar Ghosal; Hong Liu; Suzanne Prentice; Vanessa Lama; Feiyi Wang (J. Supercomput., 2025)

Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization. - Weizhi Gao; Zhichao Hou; Junqi Yin; Feiyi Wang; Linyu Peng; Xiaorui Liu (CoRR, 2025)

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit. - Junqi Yin; Mijanur Palash; M. Paul Laiu; Muralikrishnan Gopalakrishnan Meena; John Gounley; Stephen M. de Bruyn Kops; Feiyi Wang; Ramanan Sankaran; Pei Zhang (CoRR, 2025)