Publications


Papers

2021

  • K. Raghavan, P. Balaprakash, A. Lovato, N. Rocco, and S. M. Wild. "Machine-learning-based inversion of nuclear responses." Physical Review C 103, no. 3 (2021): 035502.

  • J. Burby, J. William, Q. Tang, and R. Maulik. "Fast neural Poincaré maps for toroidal magnetic fields." Plasma Physics and Controlled Fusion 63, no. 2 (2020): 024001.

  • Ćiprijanović, A, D Kafkes, K Downey, S Jenkins, G N Perdue, S Madireddy, T Johnston, G F Snyder, and B Nord (June 2021). “DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains”. In: Monthly Notices of the Royal Astronomical Society. Vol. 506. 1, 677–691

  • Ćiprijanović, A, D Kafkes, G N Perdue, K Pedro, G Snyder, FJ Sánchez, S Madireddy, S Wild, B Nord. “Robustness of deep learning algorithms in astronomy--galaxy morphology studies” NeurIPS 2021 workshop on Machine Learning and the Physical Sciences, December, 2021

  • X. Dong, N. Ramachandra, S. Habib, K. Heitmann, M. Buehlmann, S. Madireddy. “Physical Benchmarking for AI-Generated Cosmic Web” NeurIPS 2021 workshop on AI for Science: Mind the Gaps, December, 2021

  • Hoda Shajari, Jaemoon Lee, Sanjay Ranka, Anand Rangarajan, Hybrid Generative Models for Two-Dimensional Datasets, Lecture Notes in Computer Science, 12892, Vol 623-636, June 2021.

  • H. Huang, C. Xu, S. Yoo, Interpretable temporal GANs for industrial imbalanced multivariate time series simulation and classification, Proceedings of the 36th Annual ACM Symposium on Applied Computing, 924-933, 2021

  • H. Wang, Y. Deng, S. Yoo, H. Ling, Y. Lin, AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning, Proceedings of the IEEE/CVF International Conference on Computer Vision, 7658-7667, 2021

  • H. Huang, F. Xue, W. Yan, T. Wang, S. Yoo, C. Xu, Learning Associations between Features and Clusters: An Interpretable Deep Clustering Method, 2021 International Joint Conference on Neural Networks (IJCNN), 2021

  • C. Xu, H. Huang, S. Yoo, A Deep Neural Network for Multivariate Time Series Clustering with Result Interpretation, 2021 International Joint Conference on Neural Networks (IJCNN), 2021

  • H. Huang, S. Yoo, C. Xu, Deep Clustering based on Bi-Space Association Learning, Proceedings of the 29th ACM International Conference on Multimedia, 2021

  • W. Xu, X. Luo, Y. Ren, J. H. Park, S. Yoo and B. T. Nadiga, "Feature Importance in a Deep Learning Climate Emulator, " AIMOCC workshop co-held with ICLR, 2021.

  • X. Huang, S. Jamonnak, Y. Zhao, T. H. Wu, W. Xu, "A Visual Designer of Layer-wise Relevance Propagation Models," EuroVis 2021.

  • Xiaoqi Wang, Kevin Yen, Yifan Hu, Han-Wei Shen: DeepGD: A Deep Learning Framework for Graph Drawing Using GNN, IEEE Computer Graphics and Applications (2021)

  • Yifei An, Han-Wei Shen, Guihua Shan, Guan Li, Jun Liu: STSRNet: Deep Joint Space-Time Super-Resolution for Vector Field Visualization, IEEE Computer Graphics and Applications (2021)

  • Jiayi Xu, Soumya Dutta, Wenbin He, Joachim Moortgat, and Han-Wei Shen: Geometry-Driven Detection, Tracking and Visual Analysis of Viscous and Gravitational Fingers, IEEE Transactions on Visualization and Computer Graphics (2020)

  • Hanqi Guo, David Lenz, Jiayi Xu, Xin Liang, Wenbin He, Iulian R. Grindeanu, Han-Wei Shen, Tom Peterka, Todd Munson, and Ian Foster, "FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking." IEEE Transactions on Visualization and Computer Graphics, 27(8):3463-3480, 2021.

  • Jiayi Xu, Hanqi Guo, Han-Wei Shen, Mukund Raj, Xueqiao Xu, Xueyun Wang, Zhehui Wang, and Tom Peterka, "Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis." IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE PacificVis 2021), 27(6):2808-2820, 2021. (Best Paper Award in IEEE PacificVis 2021)

  • Khaled Z. Ibrahim, Tan Nguyen, Hai Ah Nam, Wahid Bhimji, Steven Farrell, Leonid Oliker, Michael Rowan, Nick Wright, Samuel Williams. "Architectural Requirements for Deep-learning Workloads in HPC Environments", 12th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, 2021. (Best Paper Award)

  • R. Van Beeumen, K.Z. Ibrahim, G.D. Kahanamoku-Meyer, N.Y. Yao, and C. Yang, “Enhancing Scalability of a Matrix-Free Eigensolver for Studying Many-Body Localization”, accepted, International Journal of High Performance Computing Applications, 2021, arXiv:2012.00217

  • Hanqi Guo and Thomas Peterka, "Exact Analytical Parallel Vectors." In Proceedings of 2021 IEEE VIS Short Papers, pages 101-105, 2021. (Best Short Paper Honorable Mention)

  • Yang Zhang, Hanqi Guo, Lanyu Shang, Dong Wang, and Tom Peterka, "A Multi-branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data." IEEE Transactions on Big Data, 2021. (In Preprint)

  • Dmitriy Morozov, Tom Peterka, Hanqi Guo, Mukund Raj, Jiayi Xu, and Han-Wei Shen, "IExchange: Asynchronous Communication and Termination Detection for Iterative Algorithms." In LDAV'21: Proceedings of IEEE Symposium on Large Data Analysis and Visualization, 2021.

  • Sunwoo Lee, Qiao Kang, Kewei Wang, Jan Balewski, Alex Sim, Ankit Agrawal, Alok Choudhary, Peter Nugent, Kesheng Wu, and Wei-keng Liao. Asynchronous I/O Strategy for Large-Scale Deep Learning Applications. In the 28th International Conference on High-Performance Computing, Data, and Analytics (HiPC), December 2021.

  • Pascal Grosset and James Ahrens. 2021. Lightweight Interface for In Situ Analysis and Visualization of Particle Data. In ISAV'21: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV'21). Association for Computing Machinery, New York, NY, USA, 12–17. DOI:https://doi.org/10.1145/3490138.3490143

  • Chunhua Liao, Anjia Wang, Giorgis Georgakoudis, Bronis R. de Supinski,Yonghong Yan, David Beckingsale, and Todd Gamblin, Extending OpenMP for Machine Learning-Driven Adaptation, Eighth Workshop on Accelerator Programming Using Directives (WACCPD), November 2021.

  • Jean Luca Bez, Houjun Tang, Bing Xie, David Williams-Young, Rob Latham, Rob Ross, Sarp Oral, and Suren Byna, "I/O Bottleneck Detection and Tuning: Connecting the Dots using Interactive Log Analysis", 6th International Parallel Data Systems Workshop (PDSW) 2021, held in conjunction with SC21

  • Xingfu Wu and Valerie Taylor, Utilizing Ensemble Learning for Performance and Power Modeling and Improvement of Parallel Cancer Deep Learning CANDLE Benchmarks, Concurrency and Computation Practice and Experience, July 2021, e6516, https://doi.org/10.1002/cpe.6516.

  • Xingfu Wu, Valerie Taylor, and Zhiling Lan, Performance and Energy Improvement of the ECP Proxy App SW4lite under Various Workloads, SC2021 Workshop on Memory-Centric High Performance Computing (MCHPC’21), Nov. 2021.

  • Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Valerie Taylor, Paul Hovland, and May Hall, Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization, Concurrency and Computation Practice and Experience, Nov. 2021, e6683, https://doi.org/10.1002/cpe.6683.

  • Timur Takhtaganov, Zarija Lukic, Juliane Mueller, Dmitriy Morozov. Cosmic Inference: Constraining Parameters with Observations and a Highly Limited Number of Simulations. The Astrophysical Journal, Volume 906, Number 2, 2021.

  • Nan Ding, Muaaz Awan, Samuel Williams, "Instruction Roofline: An insightful visual performance model for GPUs", CCPE, August 2021.

  • Nan Ding, Yang Liu, Samuel Williams, Xiaoye S. Li, "A Message-Driven, Multi-GPU Parallel Sparse Triangular Solver", SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21), July 2021.

  • Charlene Yang, Yunsong Wang, Thorsten Kurth, Steven Farrell, Samuel Williams, "Hierarchical Roofline Performance Analysis for Deep Learning Applications", Intelligent Computing, LNNS, July 2021.

  • Dmitriy Morozov, Tom Peterka, Hanqi Guo, Mukund Raj, Jiayi Xu, Han-Wei Shen. IExchange: Asynchronous Communication and Termination Detection for Iterative Algorithms. Proceedings of IEEE LDAV’21 Symposium on Large Data Analysis and Visualization, New Orleans, LA, 2021.

  • Patrick Diehl, Gregor Daiß, Dominic Marcello, Kevin Huck, Sagiv Shiber, Hartmut Kaiser, Juhan Frank, Geoffrey C. Clayton, Dirk Pflüger, Octo-Tiger’s New Hydro Module and Performance Using HPX+CUDA on ORNL’s Summit, 2021 IEEE International Conference on Cluster Computing (CLUSTER), 2021, pp. 204-214, doi: 10.1109/Cluster48925.2021.00059.

  • Weile Wei, Eduardo D'Azevedo, Kevin Huck, Arghya Chatterjee, Oscar Hernandez, and Hartmut Kaiser. 2021. Memory reduction using a ring abstraction over GPU RDMA for distributed quantum Monte Carlo solver. Proceedings of the Platform for Advanced Scientific Computing Conference. Association for Computing Machinery, New York, NY, USA, Article 14, 1–9. DOI:https://doi.org/10.1145/3468267.3470618

  • Patrick Diehl, Dominic Marcello, Parsa Amini, Hartmut Kaiser, Sagiv Shiber, Geoffrey C. Clayton, Juhan Frank, Gregor Daiß, Dirk Pflüger, David Eder, Alice Koniges, Kevin Huck. Performance Measurements Within Asynchronous Task-Based Runtime Systems: A Double White Dwarf Merger as an Application, in Computing in Science & Engineering, vol. 23, no. 3, pp. 73-81, 1 May-June 2021, doi: 10.1109/MCSE.2021.3073626.

  • Gregory Herschlag, Seyong Lee, Jeffrey S. Vetter, and Amanda Randles, Analysis of GPU Data Access Patterns on Complex Geometries for the D3Q19 Lattice Boltzmann Algorithm, Transactions on Parallel and Distributed Systems (TPDS), 2021. DOI: 10.1109/TPDS.2021.3061895

  • Anthony Cabrera, Seth Hitefield, Jungwon Kim, Seyong Lee, Narasinga Rao Miniskar, and Jeffrey S. Vetter, Toward Performance Portable Programming for Heterogeneous System-on-Chips: Case Study with Qualcomm Snapdragon SoC, The IEEE High Performance Extreme Computing Conference (HPEC ‘21), 2021.

  • Mohammad Alaul Haque Monil, Seyong Lee, Jeffrey S. Vetter, and Allen D. Malony, Comparing LLC-memory Traffic between CPU and GPU Architectures, RSDHA: Redefining Scalability for Diversely Heterogeneous Architectures, in conjunction with SC21, 2021.

  • Pradeep Subedi, Philip E. Davis, and Manish Parashar, RISE: Reducing I/O Contention in Staging-based Extreme-Scale In-situ Workflows. In 2021 IEEE International Conference on Cluster Computing (CLUSTER) (pp. 146-156). IEEE.

  • Zhe Wang, Praeep Subedi, Matthieu Dorier, Philip E. Davis, and Manish Parashar, 2021, June. Facilitating Staging-based Unstructured Mesh Processing to Support Hybrid In-Situ Workflows. In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 960-964). IEEE.

  • Zhe Wang, Praeep Subedi, Matthieu Dorier, Philip E. Davis, and Manish Parashar, Adaptive Placement of Data Analysis Tasks For Staging Based In-Situ Processing. In the 28th International Conference on High-Performance Computing, Data, and Analytics (HiPC), December 2021.

  • E. Wes Bethel, Colleen Heinemann, and Talita Perciano. Performance Tradeoffs in Shared-memory Platform Portable Implementations of a Stencil Kernel. In Matthew Larsen and Filip Sadlo, editors, Eurographics Symposium on Parallel Graphics and Visualization, Zu ̈rich, Switzerland, June 2021. The Eurographics Association. https://escholarship.org/uc/item/22q2t9cg.

  • Khaled Z. Ibrahim. 2021. CSPACER: A Reduced API Set Runtime for the Space Consistency Model. In The International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia 2021). Association for Computing Machinery, New York, NY, USA, 58–68. DOI:https://doi.org/10.1145/3432261.3432272

  • Brock, B., Buluç, A., Mattson, T.G., McMillan, S. and Moreira, J.E., 2021, June. Introduction to GraphBLAS 2.0. In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 253-262). IEEE.

  • Brock, B., Buluç, A., Mattson, T.G., McMillan, S. and Moreira, J.E., 2021, November. GraphBLAS C API Standard 2.0. https://graphblas.org/docs/GraphBLAS_API_C_v2.0.0.pdf

2020


  • Alok Tripathy, Katherine Yelick, and Aydın Buluç. "Reducing communication in graph neural network training." In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-14. IEEE, 2020

  • Nicolas Swenson, Aditi S Krishnapriyan, Aydın Buluç, Dmitriy Morozov, Katherine Yelick. PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction, NeurIPS Workshop, 2021. arXiv preprint arXiv:2010.16027.

  • Sunwoo Lee, Qiao Kang, Ankit Agrawal, Alok Choudhary, and Wei-keng Liao. Communication-Efficient Local Stochastic Gradient Descent for Scalable Deep Learning. In International Conference on Big Data, December 2020.

  • Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D Hauck, M Paul Laiu, and Richard Archibald. In Situ Compression Artifact Removal In Scientific Data Using Deep Transfer Learning And Experience Replay. Machine Learning: Science and Technology, 2(2), IOP Publishing Ltd, December 2020.

  • Yunsong Wang, Charlene Yang, Steven Farrell, Yan Zhang, Thorsten Kurth, Samuel Williams, "Time-Based Roofline for Deep Learning Performance Analysis", Deep Learning on Supercomputing (DLonSC), November 2020.

  • Wenbin He, Junpeng Wang, Hanqi Guo, Ko-Chih Wang, Han-Wei Shen, Mukund Raj, Youssef S. G. Nashed, and Tom Peterka, “InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations.” IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS 2019), 26(1):23-33, 2020.

  • Wenbin He, Hanqi Guo, Han-Wei Shen, and Tom Peterka, “eFESTA: Ensemble Feature Exploration with Surface Density Estimates.” IEEE Transactions on Visualization and Computer Graphics, 26(4):1716-1731, 2020.

  • Pascal Grosset, Jesus Pulido, James Ahrens, "Personalized In Situ Steering for Analysis and Visualization", In Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV '20). Association for Computing Machinery, New York, NY, USA. DOI:https://doi.org/10.1145/3426462.3426463

  • Xin Liang, Hanqi Guo, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, and Tom Peterka. “Toward Feature Preserving 2D and 3D Vector Field Compression.” In Proceedings of IEEE Pacific Visualization Symposium, pages 81-90, 2020.

  • Wenbin He, Junpeng Wang, Hanqi Guo, Han-Wei Shen, and Tom Peterka, “CECAV: Collective Ensemble Comparison and Visualization using Deep Neural Networks.” Journal of Visual Informatics, Journal of Visual Informatics, 4(2):109--121, 2020.

  • Zhehui Wang, Jiayi Xu, Yao E. Kovach, Bradley T. Wolfe, Edward Thomas Jr., Hanqi Guo, John E. Foster, and Han-Wei Shen, “Microparticle cloud imaging and tracking for data-driven plasma science.” Physics of Plasmas, AIP Publishing, 27(3):033703, 2020.

  • Chad A. Steed, John R. Goodall, Junghoon Chae, and Artem Trofimov. “CrossVis: A Visual Analytics System for Exploring Heterogeneous Multivariate Data with Applications to Materials and Climate Sciences”, Graphics & Visual Computing, 3:20013, 2020.

  • Qiao Kang, Alex Sim, Peter Nugent, Sunwoo Lee, Wei-Keng Liao, Ankit Agrawal, Alok Choudhary and Kesheng Wu. “Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow”. In the 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, May 2020.

  • Nan Ding, Samuel Williams, Yang Liu, Xiaoye S. Li, "Leveraging One-Sided Communication for Sparse Triangular Solvers", 2020 SIAM Conference on Parallel Processing for Scientific Computing, February 14, 2020.

  • M. Isakov, E. Rosario, S. Madireddy, P. Balaprakash, P. Carns, R. Ross, M. Kinsy. “HPC I/O Throughput Bottleneck Analysis with Explainable Local Models.” In SC ’20: IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis, 2020.

  • R. Maulik, N. A. Garland, J. W. Burby, X. Tang, and P. Balaprakash. "Neural network representability of fully ionized plasma fluid model closures." Physics of Plasmas 27, no. 7 (2020): 072106.

  • R. Maulik, A. Mohan, B. Lusch, S. Madireddy, P. Balaprakash, and D. Livescu. "Time-series learning of latent-space dynamics for reduced-order model closure." Physica D: Nonlinear Phenomena 405 (2020): 132368.

  • R. Maulik, B. Lusch, and P. Balaprakash. "Non-autoregressive time-series methods for stable parametric reduced-order models." Physics of Fluids, 32, 087115 (2020).

  • Zhe Wang, Pradeep Subedi, Matthieu Dorier, Philip E. Davis, and Manish Parashar. “Staging Based Task Execution For Data Driven In-Situ Scientific Workflows”. 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020.

  • Anjia Wang, Alok Mishra, Chunhua Liao, Yonghong Yan, and Barbara Chapman, "FreeCompilerCamp.org: Training for OpenMP Compiler Development from Cloud", Volume 11, Issue 1, pp. 53 - 60, Journal of Computational Science Education, Jan. 2020

  • Gleison Souza Diniz Mendonça, Chunhua Liao, and Fernando Magno Quintão Pereira. “AutoParBench: a Unified Test Framework for OpenMP-based Parallelizers.” In Proceedings of the 34th ACM International Conference on Supercomputing (ICS '20). Association for Computing Machinery, New York, NY, USA, Article 28, 1–10.

  • A. Lasa, J. Canik, S. Blondel, T. Younkin, D. Curreli, J. Drobny, P.C. Roth, M. Cianciosa, W. Elwasif, D. Green, B. Wirth, “Multi-physics Modeling of the Long-term Evolution of Helium Plasma Exposed Surfaces,” Physica Scripta T171:014041, January 2020

  • S.F. Siegel, Y. Yan. “Action-based Model Checking: Logic, Automata, and Reduction”. Computer Aided Verification (CAV 2020), LLNCS 12225, Springer, 77–100, 2020

  • Jacob Lambert, Seyong Lee, Jeffrey S. Vetter, and Allen D. Malony, “MPACC: An Integrated Translation and Optimization Framework for OpenACC and OpenMP”, SC 2020: The International Conference for High Performance Computing, Networking, Storage, and Analysis, 2020.

  • Roberto Gioiosa, Burcu O. Mutlu, Seyong Lee, Jeffrey S. Vetter, Giulio Picierro, and Marco Cesati, “The Minos Computing Library: Efficient Parallel Programming for Extremely Heterogeneous Systems”, Proceedings of the 13th Annual Workshop on General Purpose Processing using Graphics Processing Unit (GPGPU’20), 2020.

  • K. Meng and B. Norris. Guiding optimizations with Meliora: A deep walk down memory lane. Proceedings of LCPC 2020: Workshop on Languages and Compilers for Parallel Computing, October 14-16, 2020, 10 2020.

  • W. Wei, A. Chatterjee, K. Huck, O. Hernandez and H. Kaiser, "Performance Analysis of a Quantum Monte Carlo Application on Multiple Hardware Architectures Using the HPX Runtime," 2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA), GA, USA, 2020, pp. 77-84, doi: 10.1109/ScalA51936.2020.00015.

2019

  • Hanqi Guo, Wenbin He, Sangmin Seo, Han-Wei Shen, Emil Mihai Constantinescu, Chunhui Liu, and Tom Peterka, “Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis.” IEEE Transactions on Visualization and Computer Graphics, 25(9):2710-2724, 2019.

  • Jun Tao, Martin Imre, Chaoli Wang, Nitesh V. Chawla, Hanqi Guo, Gökhan Sever, and Seung Hyun Kim, “Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps.” IEEE Transactions on Visualization and Computer Graphics, 25(1):1236-1245, 2019.

  • Junghoon Chae, Debsindhu Bhowmik, Heng Ma, Arvind Ramanathan, and Chad A. Steed. “Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations”, In Proceedings of the IEEE International Conference on Big Data, Dec. 2019.

  • Junghoon Chae, Chad A. Steed, John Goodall, and Steven Hahn. “Dynamic Color Mapping with a Multi-scale Histogram: A Design Study with Physical Scientists”, In Proceedings of the SPIE Visualization and Data Analysis Conference, Jan. 2019.

  • Artem A. Trofimov, Alison A. Pawlicki, Nikolay Borodinov, Shovon Mandal, Teresa J. Mathews, Mark Hildebrand, Maxim A. Ziatdinov, Katherine A. Hausladen, Paulina K. Urbanowicz, Chad A. Steed, Anton V. Ievlev, Alex Belianinov, Joshua K. Michener, Rama Vasudevan, and Olga S. Ovchinnikova. “Deep Data Analytics for Genetic Engineering of Diatoms Linking Genotype to Phenotype via Machine Learning”, npj Computational Materials, 5:4, 2019.

  • Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok Choudhary, Richard Archibald, and Wei-keng Liao. “Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time”. In the IEEE International Conference on Big Data, December 2019.

  • Khaled Ibrahim, Samuel Williams, Leonid Oliker, "Performance Analysis of GPU Programming Models using the Roofline Scaling Trajectories", International Symposium on Benchmarking, Measuring and Optimizing (Bench), BEST PAPER AWARD, November 2019

  • Nan Ding, Samuel Williams, "An Instruction Roofline Model for GPUs", Performance Modeling, Benchmarking, and Simulation (PMBS), BEST PAPER AWARD, November 18, 2019

  • Charlene Yang, Thorsten Kurth, Samuel Williams, "Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC-9 Perlmutter system", Concurrency and Computation: Practice and Experience (CCPE), August 2019.

  • Charlene Yang, Thorsten Kurth, Samuel Williams, "Hierarchical Roofline Analysis for GPUs: Accelerating Performance Optimization for the NERSC-9 Perlmutter System", Cray User Group (CUG), May 2019.

  • Allen D. Malony, Srinivasan Ramesh, Kevin Huck, Nicholas Chaimov, and Sameer Shende. 2019. "A Plugin Architecture for the TAU Performance System." In Proceedings of the 48th International Conference on Parallel Processing (ICPP 2019). Association for Computing Machinery, New York, NY, USA, Article 90, 1–11. DOI:https://doi.org/10.1145/3337821.3337916

  • D. Boehme, K. Huck, J. Madsen and J. Weidendorfer, "The Case for a Common Instrumentation Interface for HPC Codes," 2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), Denver, CO, USA, 2019, pp. 33-39, doi: 10.1109/ProTools49597.2019.00010.

  • S. Madireddy, P. Balaprakash, P. Carns, R. Latham, G. K. Lockwood, R. Ross, S. Snyder, and S. M. Wild. "Adaptive Learning for Concept Drift in Application Performance Modeling." In Proceedings of the 48th International Conference on Parallel Processing, pp. 1-11. 2019.

  • J. Wang, P. Balaprakash, and R. Kotamarthi. Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model. Geoscientific Model Development, 2019:1–31, 2019.

  • S. Madireddy, N. Li, N. Ramachandra, P. Balaprakash, and S. Habib. "Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images." In ML and the Physical Sciences Workshop at NeurIPS, 2019.

  • R. Maulik, V. Rao, S. Madireddy, B. Lusch, and P. Balaprakash. "Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models." In ML and the Physical Sciences Workshop at NeurIPS, 2019.

  • Wan, Lipeng, Mehta, Kshitij V., Klasky, Scott A., Wolf, Matthew D., Wang, H Y., Wang, W H., Li, J C., and Lin, Zhihong. Mon . "Data Management Challenges of Exascale Scientific Simulations: A Case Study with the Gyrokinetic Toroidal Code and ADIOS". United States. https://www.osti.gov/servlets/purl/1558473.

  • Pradeep Subedi, Philip E. Davis, and Manish Parashar. "Leveraging Machine Learning for Anticipatory Data Delivery in Extreme Scale In-situ Workflows." 2019 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2019.

  • Yonghong Yan, Anjia Wang, Chunhua Liao, Tom Scogland and Bronis R. de Supinski, “Extending OpenMP Metadirective Semantics for Runtime Adaptation”, Fifteenth International Workshop on OpenMP (IWOMP 2019), Auckland, New Zealand, September 11–13, 2019

  • Jie Ren, Chunhua Liao and Dong Li, Opera: Data Access Pattern Similarity Analysis To Optimize OpenMP Task Affinity, 24th International Workshop On High-level Parallel Programming Models And Supportive Environments (HIPS), Held in Conjunction With 33rd IPDPS International Parallel & Distributed Processing Symposium, May 20-24, 2019, Rio De Janeiro, Brazil

  • P.C. Roth, “Improved Accuracy for Automated Communication Pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning,” In A. Bhatele, D. Boehme, J. Levine, A. Malony, M. Schulz (eds) Programming and Performance Visualization Tools, Lecture Notes in Computer Science 11027, Springer, Cham, pp. 38-55, 2019

  • P.C. Roth, K. Huck, G. Gopalakrishnan, F. Wolf, “Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges,” In A. Bhatele, D. Boehme, J. Levine, A. Malony, M. Schulz (eds) Programming and Performance Visualization Tools, Lecture Notes in Computer Science 11027, Springer, Cham, pp. 265-272, 2019

  • W. Elwasif, A. Lasa, P.C. Roth, T. Younkin, M. Cianciosa, “Nested Workflows for Loosely Coupled HPC Simulations,” 16th ACES/IEEE International Conference on Computer Systems and Applications (AICCSA 2019), November 2019

  • S.F. Siegel, “What’s Wrong with On-the-fly Partial Order Reduction”. Computer Aided Verification (CAV 2019), LLNCS 11562, Springer, 478–495, 2019

  • Forrest Shriver, Seyong Lee, Steven Hamilton, Jeffrey Vetter, and Justin Watson, VEXS, “An Open Platform for the Study of Continuous-Energy Neutron Transport Cross-Section Lookup Algorithms on GPUs”, MC19: International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, 2019.

  • Seyong Lee, John Gounley, Amanda Randles, and Jeffrey S. Vetter, “Performance Portability Study for Massively Parallel Computational Fluid Dynamics Application on Scalable Heterogeneous Architectures”, Journal of Parallel and Distributed Computing (JPDC), 2019.

2018

  • M. Dorier, P. Carns, K. Harms, R. Latham, R. Ross, S. Snyder, J. Wozniak, S.K. Gutierrez, B. Robey, B. Settlemyer, G. Shipman, J. Soumagne, J. Kowalkowski, M. Paterno, and S. Sehrish. Methodology for the Rapid Development of Scalable HPC Data Services. In Proceedings of the 3rd Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems, November 2018.

  • Junpeng Wang, Liang Gou, Han-Wei Shen, Hao Yang : DQNViz: A Visual Analytics Approach to Understand Deep Q-Networks, IEEE Transactions on Visualization and Computer Graphics, (Accepted at IEEE VAST 2018) [Best Paper Honorable Mention Award]

  • Junpeng Wang, Subhashis Hazarika, Cheng Li, Han-Wei Shen: Visualization and Visual Analysis of Ensemble Data: A Survey, IEEE Transactions on Visualization and Computer Graphics, 2018 (Early Access)

  • Subhashis Hazarika, Soumya Dutta, Han-Wei Shen, Jen-Peng Chen: CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data, IEEE Transactions on Visualization and Computer Graphics (IEEE SciVis 2018)

  • Wenbin He, Hanqi Guo, Tom Peterka, Sheng Di, Franck Cappello, and Han-Wei Shen: Parallel Partial Reduction for Large-Scale Data Analysis and Visualization, In Proceedings of 2018 IEEE Symposium on Large Data Analysis and Visualization, 2018. [Honorable Mention]

  • Hanqi Guo, Wenbin He, Sangmin Seo, Han-Wei Shen, Emil Mihai Constantinescu, Chunhui Liu, and Tom Peterka, "Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis." IEEE Transactions on Visualization and Computer Graphics. (Accepted)

  • Wenbin He, Hanqi Guo, Han-Wei Shen, and Tom Peterka, "eFESTA: Ensemble Feature Exploration with Surface Density Estimates." IEEE Transactions on Visualization and Computer Graphics. (Accepted)

  • Subhashis Hazarika, Ayan Biswas, Han-Wei Shen: Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models, IEEE Transactions on Visualization and Computer Graphics , 24(1): 934-943 (2018)

  • Jun Tao, Martin Imre, Chaoli Wang, Nitesh V. Chawla, Hanqi Guo, Gökhan Sever, and Seung Hyun Kim, "Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps." IEEE Transactions on Visualization and Computer Graphics (VIS' 18), 2018. (Accepted)

  • P. Balaprakash, J. Dongarra, T. Gamblin, M. Hall, J. Hollingsworth, B. Norris, R. Vuduc, "Autotuning in High-Performance Computing Applications," in Proceedings of the IEEE, vol. 106, no. 11, pp. 2068-2083, Nov. 2018.

  • Jiang Zhang, Hanqi Guo, Fan Hong, Xiaoru Yuan, and Tom Peterka, "Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing." IEEE Transactions on Visualization and Computer Graphics (VIS '17), 24(1):954-963, 2018.

  • Jiang Zhang, Hanqi Guo, Xiaoru Yuan, and Tom Peterka, "Dynamic Data Repartitioning for Load-Balanced Parallel Particle Tracing." In Proceedings of IEEE Pacific Visualization Symposium (PacificVis '18), pages 86-95, Kobe, Japan, April 10-13, 2018.

  • Hongzhang Shan, Samuel Williams, Calvin W. Johnson, "Improving MPI Reduction Performance for Manycore Architectures with OpenMP and Data Compression", Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), November 2018.

  • Pradeep Subedi, Philip Davis, Shaohua Duan, Scott Klasky, Hemanth Kolla, and Manish Parashar. "Stacker: an autonomic data movement engine for extreme-scale data staging-based in-situ workflows." In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), p. 73. IEEE Press, November 2018.

  • Sunwoo Lee, Ankit Agrawal, Prasanna Balaprakash, Alok Choudhary, and Wei-keng Liao, "Communication-Efficient Parallelization Strategy for Deep Convolutional Neural Network Training", the Workshop of Machine Learning in HPC Environments, held in conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, November 2018.

  • Charlene Yang, Rahulkumar Gayatri, Thorsten Kurth, Protonu Basu, Zahra Ronaghi, Adedoyin Adetokunbo, Brian Friesen, Brandon Cook, Douglas Doerfler, Leonid Oliker, Jack Deslippe, Samuel Williams, "An Empirical Roofline Methodology for Quantitatively Assessing Performance Portability", International Workshop on Performance, Portability and Productivity in HPC (P3HPC), November 2018.

  • P.C. Roth, K. Huck, G. Gopalakrishnan, and F. Wolf, "Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges," Fifth International Workshop on Visual Performance Analysis (VPA18), Dallas, Texas, November 2018.

  • Khaled Ibrahim, Samuel Williams, Leonid Oliker, "Roofline Scaling Trajectories: A Method for Parallel Application and Architectural Performance Analysis", HPCS Special Session on High Performance Computing Benchmarking and Optimization (HPBench), July 2018.

  • James Kress, Jong Choi, Scott Klasky, Michael Churchill, Hank Childs, and David Pugmire, "Binning Based Data Reduction for Vector Field Data of a Particle-In-Cell Fusion Simulation", ISC Workshop on In Situ Visualization (WOIV), Frankfurt, Germany, June 2018.

  • Mark Kim, James Kress, Jong Youl Choi, Norbert Podhorszki, Scott Klasky, Matthew Wolf, Kshitij Mehta, Kevin Huck, Berk Geveci, Sujin Phillip, Robert Maynard, Hanqi Guo, Thomas Peterka, Kenneth Moreland, Choong-Seock Chang, Julien Dominski, Michael Churchill and David Pugmire, "In Situ Analysis and Visualization of Fusion Simulations: Lessons Learned", ISC Workshop on In Situ Visualization (WOIV), Frankfurt, Germany, June 2018.

  • David Pugmire, Abhishek Yenpure, Mark Kim, James Kress, Robert Maynard, Hank Childs, and Bernd Hentschel, "Performance Portable Particle Advection with VTK-m," Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), Brno, Czech Republic, June 2018.

  • Kaixi Hou, Hao Wang, Wu-chun Feng, Jeffrey S. Vetter, and Seyong Lee. Highly Efficient Compensation-based Parallelism for Wavefront Loops on GPUs. 32th IEEE International Parallel & Distributed Processing Symposium (IPDPS), Vancouver, Canada, 2018.

  • Charlene Yang, Brian Friesen, Thorsten Kurth, Brandon Cook, Samuel Williams, "Toward Automated Application Profiling on Cray Systems", Cray User Group (CUG), May 2018.

  • Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydın Buluç, Dmitriy Morozov, Sang-Yun Oh, Leonid Oliker, Katherine Yelick: Communication-Avoiding Optimization Methods for Massive-Scale Graphical Model Structure Learning. 21st International Conference on Artificial Intelligence and Statistics (AISTATS), April 9 - 11, 2018, ArXiv eprints.

  • Tuomas Koskela, Jack Deslippe, Zakhar Matveev, Adetokunbo Adedoyin, Charlene Yang, Rahulkumar Gayatri, Hongzhang Shan, Zhengji Zhao, Philippe Thierry, Roman Belonov, Samuel Williams, Leonid Oliker, A Novel Multi-Level integrated Roofline Model Approach for Performance Characterization, High Performance Computing: 33rd International Conference, ISC High Performance 2018, Frankfurt, Germany, June 24–28, 2018.

  • Khaled Z Ibrahim, Samuel Williams, Leonid Oliker, Roofline Scaling Trajectories: A Method for Parallel Application and Architectural Performance Analysis, High Performance Computing Benchmarking and Optimization (HPBench 2018). As part of The 16th International Conference on High Performance Computing & Simulation (HPCS 2018), Orléans, France, July 16 – 20, 2018.

  • P.C. Roth, "Scalable, Automated Characterization of Parallel Application Communication Behavior," (presentation), 2018 Scalable Tools Workshop, Solitude, Utah, July 2018.

  • Subhashis Hazarika, Ayan Biswas, Han-Wei Shen: Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models, IEEE Transactions on Visualization and Computer Graphics , 24(1): 934-943 (2018)

  • Ko-Chih Wang, Naeem Shareef, and Han-Wei Shen: Image and Distribution Based Volume Rendering for Large Data Sets, IEEE PacificVis 2018

  • Junpeng Wang, Liang Gou , Hao Yang, and Han-Wei Shen: GANViz: A Visual Analytics Approach to Understand the Adversarial Game, IEEE Trans. Vis. Comput. Graph. [IEEE PacificVis 2018 Best Paper Award]

  • Tzu-Hsuan Wei, Soumya Dutta, and Han-Wei Shen: Information Guided Data Sampling and Recovery using Bitmap Indexing, IEEE PacificVis 2018

  • Soumya Dutta, Han-Wei Shen, and Jen-Ping Chen: In Situ Prediction Driven Feature Analysis in Jet Engine Simulations, IEEE PacificVis 2018

  • Cheng Li, Joachim Moortgat, and Han-Wei Shen: An Automatic Data Deformation Approach for Occlusion Free Egocentric Data Exploration, IEEE PacificVis 2018

  • S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and S. M. Wild, Modeling I/O Performance Variability Using Conditional Variational Auto Encoders, preprint ANL/MCS-P9070-0518, 2018.

  • S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and S. M. Wild, Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems, In: Yokota R., Weiland M., Keyes D., Trinitis C. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science, vol 10876. Springer, Cham, doi:10.1007/978-3-319-92040-5_10.

  • X Xing, B Dong, J Ajo-Franklin, and Kesheng Wu. Automated Parallel Data Processing Engine with Application to Large-Scale Feature Extraction. In: Machine Learning in HPC Environment. Nov. 2018. https://sc18.supercomputing.org/proceedings/workshops/workshop_pages/ws_mlhpce109.html

  • J Gu, S Klasky, N Podhorszki, J Qiang, K Wu. Querying Large Scientific Data Sets with Adaptable IO System ADIOS. Asian Conference on Supercomputing Frontiers, 2018. pp. 51-69.

  • P.C. Roth, "Improved Accuracy for Automated Communication Pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning," Proceedings of the 6th Workshop on Extreme-Scale Programming Tools (ESPT'17), Denver, Colorado, USA, Nov. 2017. To be published in Lecture Notes in Computer Science 11027, 2018.

  • R. Lim, B. Norris, and A. Malony. A similarity measure for GPU kernel subgraph matching. 31st International Workshop on Languages and Compilers for Parallel Computing (LCPC), Oct. 2018.

2017

  • Cheng Li, Han-Wei Shen: Winding Angle Assisted Particle Tracing in Distribution-Based Vector Field, SIGGRAPH Asia Symposium on Visualization 2017. [Best Paper Honorable Mention award]

  • Soumya Dutta, Xiaotong Liu, Ayan Biswas, Han-Wei Shen, and Jen-Ping Chen: Pointwise Information Guided Visual Analysis of Time-varying Multi-fields, SIGGRAPH Asia Symposium on Visualization 2017

  • Junpeng Wang, Xiaotong Liu, Han-Wei Shen: High-dimensional data analysis with subspace comparison using matrix visualization, Information Visualization 2017

  • Junpeng Wang, Xiaotong Liu, Han-Wei Shen, Guang Lin: Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots. IEEE Trans. Vis. Comput. Graph. 23(1): 81-90 (2017)

  • S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and S. M. Wild. Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity, The 12th International Conference on Networking, Architecture, and Storage, Shenzhen, 2017, pp. 1-10. doi:10.1109/NAS.2017.8026844.

  • Sang-Yun Oh, Alnur Ali, Penporn Koanantakool, Ariful Azad, Aydın Buluç, Dmitriy Morozov, Leonid Oliker, Katherine Yelick: Whole-brain Functional Connectivity Mapping and Region Segmentation from Distributed Estimation of Voxel-level Sparse Precision Matrix. BigNeuro Workshop @ Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, Dec 9, 2017.

  • K. Meng and B. Norris. Mira: A framework for static performance analysis. 2017 IEEE International Conference on Cluster Computing (CLUSTER), pp. 103–113, Sept 2017.

  • R. Lim, B. Norris, and A. Malony. Autotuning GPU kernels via static and predictive analysis. The 46th International Conference on Parallel Processing (ICPP), pp. 523–532, Aug 2017.

Posters

2018