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Papers
Papers
2025
H. Li and H.-W. Shen. Improving Efficiency of Iso-Surface Extraction on Implicit Neural Representations Using Uncertainty Propagation . IEEE Transactions on Visualization & Computer Graphics, 31(02):1513–1525, February 2025.
T. M. Athawale, Z. Wang, D. Pugmire, K. Moreland, Q. Gong, S. Klasky, C. R. Johnson, and P. Rosen. Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models. IEEE Transactions on Visualization and Computer Graphics, 31(1):108–118, January 2025.
M. S. Breitenfeld, H. Tang, H. Zheng, J. Henderson, and S. Byna. HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications. Int. J. High Perform. Comput. Appl.,39(1):65–78, January 2025.
Y. Mao, S. Keshavarz, M. N. T. Kilic, K. Wang, Y. Li, A. C. Reid, W. Liao, A. Choudhary, and
A. Agrawal. A Deep Learning-based Crystal Plasticity Finite Element Model. Scripta Materialia,
254:116315, January 2025.
R. Qiu, Y. Tu, P.-Y. Yen, and H.-W. Shen. VADIS: A Visual Analytics Pipeline for Dynamic Document Representation and Information-Seeking. IEEE Transactions on Visualization and Computer Graphics, 31(1):1312–1321, January 2025.
J. Shen, Y. Duan, and H.-W. Shen. SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification . IEEE Transactions on Visualization & Computer Graphics, 31(01):635–644, January 2025.
T. Xiong, S. W. Wurster, H. Guo, T. Peterka, and H.-W. Shen. Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network . IEEE Transactions on Visualization & Computer Graphics, 31(01):645–655, January 2025.
G. Eisenhauer, N. Podhorszki, A. Gainaru, S. Klasky, J. Gu, V. Bolea, L. Dulac, D. Ganyushin, W. F.Godoy, Q. Liu, C. Ross, L. Wan, S. Wittenburg, and K. Wu. HPC I/O Innovations in the Exascale Era. International Journal on High Performance Computing Applications, 2025. To Appear.
M. Landreman, J. Y. Choi, C. Alves, P. Balaprakash, R. M. Churchill, R. Conlin, and G. Roberg Clark. How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning. arXiv preprint arXiv:2502.11657, 2025.
S. Raja, I. Amin, F. Pedregosa, and A. S. Krishnapriyan. Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators. Transactions on Machine Learning Research, 2025.
J. Rudi, Y. Lee, A. H. Chadha, M. Wahib, K. Weide, J. P. O’Neal, and A. Dubey. CG-Kit: Code
Generation Toolkit for Performant and Maintainable Variants of Source Code Applied to Flash-X
Hydrodynamics Simulations. Future Generation Computer Systems, 163:107511, 2025.
M. Scot Breitenfeld, H. Tang, H. Zheng, J. Henderson, and S. Byna. HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications. The International Journal of High Performance Computing Applications, 39(1):65–78, 2025.
X. Wu, P. Balaprakash, M. Kruse, J. Koo, B. Videau, P. Hovland, V. Taylor, B. Geltz, S. Jana, and M. Hall. ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales. Concurrency and Computation: Practice and Experience, 37(1):e8322, 2025.
X. Wu, J. R. Tramm, J. Larson, J.-L. Navarro, P. Balaprakash, B. Videau, M. Kruse, P. Hovland, V. Taylor, and M. Hall. Integrating ytopt and libEnsemble to autotune OpenMC. The International Journal of High Performance Computing Applications, 39(1):79–103, 2025.
N. Ding, Y. Liu, S. Williams, and X. S. Li. A Message-Driven, Multi-GPU Parallel Sparse Triangular Solver, pages 147–159. 2025.
O. Antepara, S. Williams, M. Carlson, and J. Watkins. Performance Portable Optimizations of an Ice-sheet Modeling Code on GPU-supercomputers. In Proceedings of the SC ’24 Workshops of theInternational Conference on High Performance Computing, Network, Storage, and Analysis, SC-W’24, page 1141–1151. IEEE Press, 2025.
S. Barwey, H. Kim, and R. Maulik. Interpretable A-posteriori error indication for graph neural network surrogate models. Computer Methods in Applied Mechanics and Engineering, 433:117509, 2025.
J. M. R. Borbon, X. Wang, A. P. Di´ eguez, K. Z. Ibrahim, and B. M. Wong. VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems. Comput. Phys.Commun., 307:109403, 2025.
T. K. Dey, T. Hou, and D. Morozov. Apex Representatives. In Proceedings of Annual Symposium on Computational Geometry (SOCG), 2025.
T. K. Dey, T. Hou, and D. Morozov. A Fast Algorithm for Computing Zigzag Representatives. In Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 3530–3546. Society for Industrial and Applied Mathematics, 2025.
F. Ferdaus, X. Wu, V. Taylor, Z. Lan, S. Shanmugavelu, V. Vishwanath, and M. Papka. Evaluating Energy Efficiency of AI Accelerators Using Two MLPerf Benchmarks. In The 25th IEEE International Symposium on Cluster, Cloud, and Internet Computing (CCGrid2025), CCGrid2025, 2025.
J. Kelling, V. Bolea, M. Bussmann, A. Checkervarty, A. Debus, J. Ebert, G. Eisenhauer, V. Gutta, S. Kesselheim, S. Klasky, R. Pausch, N. Podhorszki, F. Poschel, D. Rogers, J. Rustamov, S. Schmerler,U. Schramm, K. Steiniger, R. Widera, A. Willmann, and S. Chandrasekaran. The Artificial Scientist– in-transit Machine Learning of Plasma Simulations. In IEEE International Parallel and Distributed Processing Symposium, 2025.
D. Long, S. Zhe, S. Williams, L. Oliker, and Z. Bai. Spatio-temporal Fourier Transformer (StFT) for Long-term Dynamics Prediction, 2025.
T. Nguyen, R. Shah, H. Bansal, T. Arcomano, R. Maulik, R. Kotamarthi, I. Foster, S. Madireddy, and A. Grover. Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. Advances in Neural Information Processing Systems, 37:68740–68771, 2025.
N. S. Sattar, K. Z. Ibrahim, A. Buluc¸, and S. Arifuzzaman. DyG-DPCD: A Distributed Parallel
Community Detection Algorithm for Large-Scale Dynamic Graphs. Int. J. Parallel Program., 53(1):4, 2025.
A. Yu, M. W. Mahoney, and N. B. Erichson. There is HOPE to Avoid HiPPOs for Long-memory State Space Models. In International Conference on Learning Representations, 2025.
2024
J. E. Denny, S. Lee, P. Valero-Lara, M. Gonzalez-Tallada, K. Teranishi, and J. S. Vetter. Clacc: OpenACC for C/C++ in Clang. The International Journal of High Performance Computing Applications, 38(5):427–446, October 2024.
J. Kim, S. Lee, B. Johnston, and J. S. Vetter. IRIS: A Performance-Portable Framework for
Cross-Platform Heterogeneous Computing. IEEE Transactions on Parallel & Distributed Systems,
35(10):1796–1809, October 2024.
K. Wang, V. Gupta, C. S. Lee, Y. Mao, M. N. T. Kilic, Y. Li, Z. Huang, W. Liao, A. Choudhary,
and A. Agrawal. XElemNet: Towards explainable AI for deep neural networks in materials science.
Scientific Reports, 14:25178, October 2024.
C. Lee, V. Hewes, G. Cerati, K. Wang, A. Aurisano, A. Agrawal, A. Choudhary, and W. Liao. Addressing GPU Memory Limitations for Graph Neural Networks in High-Energy Physics Applications. Frontiers in High Performance Computing, 2:1458674, September 2024.
A. Aurisano, V. Hewes, G. Cerati, J. Kowalkowski, C. S. Lee, W. Liao, D. Grzenda, K. Gumpula, and X. Zhang. Graph Neural Network For Neutrino Physics Event Reconstruction. Physical Review D, 110:032008, August 2024.
F. Samsel, W. A. Scott, and K. Moreland. A New Default Colormap for ParaView. IEEE Computer Graphics and Applications, 44(4):150–160, July 2024.
C. Chang, S. Ku, R. Hager, J. Choi, D. Pugmire, S. Klasky, A. Loarte, and R. Pitts. Role of turbulent separatrix tangle in the improvement of the integrated pedestal and heat exhaust issue for stationary-operation tokamak fusion reactors. Nuclear Fusion, 64(5), May 2024.
V. Gupta, K. Choudhary, B. DeCost, F. Tavazza, C. Campbell, W. Liao, A. Choudhary, and
A. Agrawal. Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets. npj Computational Materials, 10:1, January 2024.
J. Shen and H.-W. Shen. PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data. IEEE Transactions on Visualization and Computer Graphics, 30(1):986–996, January 2024.
S. W. Wurster, T. Xiong, H.-W. Shen, H. Guo, and T. Peterka. Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization . IEEE Transactions on Visualization & Computer Graphics, 30(01):965–974, January 2024.
T. M. Athawale, Z. Wang, D. Pugmire, K. Moreland, Q. Gong, S. Klasky, C. R. Johnson, and P. Rosen.Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models. IEEE Transactions on Visualization and Computer Graphics, 2024.
H. Ather, S. Berkman, G. Cerati, M. Kortelainen, K. H. M. Kwok, S. Lantz, S. Lee, B. Norris, M. Reid, A. R. Hall, D. Riley, A. Strelchenko, and C. Wang. Exploring code portability solutions for HEP with a particle tracking test code. Journal of Frontiers in Big Data, 7, 2024.
Z. Bai, X. Wei, W. Tang, L. Oliker, Z. Lin, and S. Williams. FTL: Transfer Learning Nonlinear
Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural Networks. (submitted to) Machine Learning: Science and Technology, 2024.
J. L. Bez, H. Tang, S. Breitenfeld, H. Zheng, W.-K. Liao, K. Hou, Z. Huang, and S. Byna. h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre-exascale platforms. Concurrency and Computation: Practice and Experience, 36(16):e8046, 2024.
P. Diehl, G. Daiß, K. Huck, D. Marcello, S. Shiber, H. Kaiser, and D. Pfl¨ uger. Simulating stellar merger using HPX/Kokkos on A64FX on Supercomputer Fugaku. The Journal of Supercomputing, 80(12):16947–16978, 2024.
R. Egele, F. Mohr, T. Viering, and P. Balaprakash. The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization. Neurocomputing, 597:127964, 2024.
R. Han, M. Zheng, S. Byna, H. Tang, B. Dong, D. Dai, Y. Chen, D. Kim, J. Hassoun, and D. Thorsley. PROV-IO++: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems. IEEE Transactions on Parallel and Distributed Systems, 35(5):844–861, 2024.
X. Jiang, R. Sengupta, J. Demmel, and S. Williams. Large scale multi-GPU based parallel traf fic simulation for accelerated traffic assignment and propagation. Transportation Research Part C: Emerging Technologies, 169:104873, 2024.
S. Kruger, E. Howell, C. Akcay, T. Bechtel Amara, J. McClenaghan, L. Lao, S. Smith, et al. Thinking Bayesian for plasma physicists. Physics of Plasmas, 31(5), 2024.
F. Lan, B. Gamelin, L. Yan, J. Wang, B. Wang, and H. Guo. Topological Characterization and Uncertainty Visualization of Atmospheric Rivers. Comput. Graph. Forum, 43(3), 2024.
X. Liu, M. Ruttgers, A. Quercia, R. Egele, E. Pfaehler, R. Shende, M. Aach, W. Schr¨ oder, P. Balaprakash, and A. Lintermann. Refining computer tomography data with super-resolution networksto increase the accuracy of respiratory flow simulations. Future Generation Computer Systems, 159:474–488, 2024.
S. Madireddy, C. Akcay, S. E. Kruger, T. B. Amara, X. Sun, J. McClenaghan, J. Koo, A. Samaddar, Y. Liu, P. Balaprakash, et al. EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D. Physics of Plasmas, 31(9), 2024.
J. McClenaghan, C. Akcay, T. Amara, X. Sun, S. Madireddy, L. Lao, S. Kruger, and O. Meneghini.Augmenting machine learning of Grad–Shafranov equilibrium reconstruction with Green’s functions. Physics of Plasmas, 31(8), 2024.
K. Moreland, T. M. Athawale, V. Bolea, M. Bolstad, E. Brugger, H. Childs, A. Huebl, L.-T. Lo,
B. Geveci, N. Marsaglia, S. Philip, D. Pugmire, S. Rizzi, Z. Wang, and A. Yenpure. Visualization at
exascale: Making it all work with VTK-m. The International Journal of High Performance Comput-
ing Applications, 38(5):508–526, 2024.
A. Nigmetov and D. Morozov. Topological optimization with big steps. Discrete & Computational Geometry, 72(1):310–344, 2024.
K. Raghavan, M. L. Avila, P. Balaprakash, H. Jayatissa, and D. Santiago-Gonzalez. Classification of events from α-induced reactions in the MUSIC detector via statistical and ML methods. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1058:168786, 2024.
K. Raghavan and A. Lovato. Uncertainty-quantification-enabled inversion of nuclear responses. Physical Review C, 110(2):025504, 2024.
M. M. Rahman, Z. Bai, J. R. King, C. R. Sovinec, X. Wei, S. Williams, and Y. Liu. Sparsified time-dependent Fourier neural operators for fusion simulations. Physics of Plasmas, 31(12):123902, 12 2024.
V. Reshniak, E. Ferguson, Q. Gong, N. Vidal, R. Archibald, and S. Klasky. Lifting MGARD: Construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order. Applied Mathematics for Modern Challenges, 2(4):409–432, 2024.
Y. Sun, E. Cucuzzella, S. Brus, S. H. K. Narayanan, B. Nadiga, L. Van Roekel, J. H¨ uckelheim, S. Madireddy, and P. Heimbach. Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates. In Proceedings of the Platform for Advanced Scientific Computing Conference, pages 1–10, 2024.
Y. Sun, O. Sowunmi, R. Egele, S. H. K. Narayanan, L. Van Roekel, and P. Balaprakash. Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach. Mathematics, 12(10):1483, 2024.
P. Valero-Lara, S. Lee, M. Gonzalez-Tallada, J. Denny, K. Teranishi, and J. S. Vetter. Enhancing Kokkos with OpenACC. The International Journal of High Performance Computing Applications, 38(5):409–426, 2024.
L. Yan, H. Guo, T. Peterka, B. Wang, and J. Wang. TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones. IEEE Trans. Vis. Comput. Graph., 30(1):1249–1259, 2024.
O. Yildiz, K. Raghavan, H. Chan, M. J. Cherukara, P. Balaprakash, S. Sankaranarayanan, and T. Peterka. Automated defect identification in coherent diffraction imaging with smart continual learning. Neural Computing and Applications, 36(35):22335–22346, 2024.
D. Yokelson, O. Lappi, S. Ramesh, M. S. V¨ ais¨ al¨ a, K. Huck, T. Puro, B. Norris, M. Korpi-Lagg,K. Heljanko, and A. D. Malony. SOMA: Observability, monitoring, and in situ analytics for exascale applications. Concurrency and Computation: Practice and Experience, 36(19):e8141, 2024.
R. Latham, R. B. Ross, P. Carns, S. Snyder, K. Harms, K. Velusamyi, P. Coffman, and G. McPheeters. Initial experiences with DAOS object storage on Aurora. In Proceedings of the 9th International Parallel Data Systems Workshop, November 2024.
A. L. Day, C. B. Wahl, R. dos Reis, W. Liao, V. P. Dravid, A. Choudhary, and A. Agrawal. Automated Nanoparticle Image Processing Pipeline for AI-Driven Materials Characterization. In Proceedings of 33rd ACM International Conference on Information and Knowledge Management (CIKM), pages 4462–4469, October 2024.
R. Sisneros, T. Athawale, D. Pugmire, and K. Moreland. An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations. In Proceedings IEEE Workshop on Uncertainty Visualization, pages 78–83, October 2024.
J. Hammer, T. Hobson, D. Pugmire, S. Klasky, K. Moreland, and J. Huang. A Personalized AI
Assistant For Intuition-Driven Visual Explorations. In IEEE 20th International Conference on e-
Science (e-Science), September 2024.
D. Pugmire, K. Moreland, T. M. Athawale, J. Hammer, and J. Huang. Top Research Challenges and Opportunities for Near Real-Time Extreme-Scale Visualization of Scientific Data. In Proceedings IEEE 20th International Conference on e-Science, September 2024.
D. Pugmire, J. Y. Choi, S. Klasky, K. Moreland, E. Suchyta, T. M. Athawale, Z. Wang, C.-S. Chang, S.-H. Ku, and R. Hager. Performance Improvements of Poincar´ e Analysis for Exascale Fusion Simu lations. In VisGap - The Gap between Visualization Research and Visualization Software, May 2024.
S. Tsalikis, W. Schroeder, D. Szafir, and K. Moreland. An Accelerated Clip Algorithm for Unstructured Meshes: A Batch-Driven Approach. In Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), May 2024.
H. Li, I. J. Michaud, A. Biswas, and H.-W. Shen. Efficient Level-Crossing Probability Calculation for Gaussian Process Modeled Data . In 2024 IEEE 17th Pacific Visualization Conference (PacificVis), IEEE Computer Society. pages 252–261, Los Alamitos, CA, USA, April 2024.
H. Ather, J. L. Bez, Y. Xia, and S. Byna. Drilling Down I/O Bottlenecks with Cross-layer I/O Profile Exploration. In 2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 532–543, 2024.
J. Bae, J. Y. Choi, M. L. Pasini, K. Mehta, P. Zhang, and K. Z. Ibrahim. MDLoader: A Hybrid
Model-Driven Data Loader for Distributed Graph Neural Network Training. In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, Atlanta, GA, USA, November 17-22, 2024, pages 1046–1057. IEEE, 2024.
J. M. R. Borbon, X. Wang, A. P. Di´ eguez, K. Z. Ibrahim, and B. M. Wong. TRAVOLTA: GPU
acceleration and algorithmic improvements for constructing quantum optimal control fields in photoexcited systems. Comput. Phys. Commun., 296:109017, 2024.
N. Chalapathi, Y. Du, and A. S. Krishnapriyan. Scaling physics-informed hard constraints with
mixture-of-experts. In The Twelfth International Conference on Learning Representations, 2024.
H. Chang, A. Samaddar, and S. Madireddy. BPNAS: Bayesian Progressive Neural Architecture Search. 2024. ICML 2024 Workshop on Differentiable Almost Everything: Differentiable Relax ations, Algorithms, Operators, and Simulators.
M. Choi, M. Okyay, A. P. Di´ eguez, M. D. Ben, K. Z. Ibrahim, and B. M. Wong. QRCODE: Massively parallelized real-time time-dependent density functional theory for periodic systems. Comput. Phys.Commun., 305:109349, 2024.
C. Coti, Y. Pfau-Kempf, M. Battarbee, U. Ganse, S. Shende, K. Huck, J. Rodriquez, L. Kotipalo, J. Faj, J. J. Williams, et al. Integration of modern HPC performance tools in vlasiator for exascale analysis and optimization. In 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pages 996–1005. IEEE, 2024.
A. P. Dieguez, M. Choi, M. Okyay, M. D. Ben, B. M. Wong, and K. Z. Ibrahim. Cost-Effective
Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality. In IEEE International Parallel and Distributed Processing Symposium, IPDPS 2024 - Workshop, San Francisco, CA, USA, May 27-31, 2024, pages 792–801. IEEE, 2024.
B. Dong, A. Nayak, V. R. Tribaldos, K. Wu, J. Ajo-Franklin, Q. Zhang, F. Guo, S. Byna, P. Dob-
son, and A. Sim. TensorSearch: Parallel Similarity Search on Tensors. In 2024 IEEE International
Conference on Big Data (BigData), pages 539–548, 2024.
B. Dong, K. Wu, and S. Byna. The Art of Sparsity: Mastering High-Dimensional Tensor Storage. In 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW),pages 439–446, 2024.
Y. Du, N. Chalapathi, and A. S. Krishnapriyan. Neural Spectral Methods: Self-supervised learning in the spectral domain. In The Twelfth International Conference on Learning Representations, 2024.
G. Eisenhauer, N. Podhorszki, A. Gainaru, S. Klasky, P. E. Davis, M. Parashar, M. Wolf, E. Suchtya,E. Fredj, V. Bolea, F. P¨ oschel, K. Steiniger, M. Bussmann, R. Pausch, and S. Chandrasekaran. Streaming Data in HPC Workflows Using ADIOS. In IEEE International Parallel and Distributed Processing Symposium, 2024.
B. Erichson, S. H. Lim, W. Xu, F. Utrera, Z. Cao, and M. Mahoney. NoisyMix: Boosting model robustness to common corruptions. In International Conference on Artificial Intelligence and Statistics, pages 4033–4041. PMLR, 2024.
Y. Etchi, D. Wang, P. Grosset, T. L. Turton, J. Ahrens, and D. Rogers. An Exploration of How
Volume Rendering is Impacted by Lossy Data Reduction. In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 250–259, 2024.
Q. Gong, Z. Wang, V. Reshniak, X. Liang, J. Chen, Q. Liu, T. M. Athawale, Y. Ju, A. Rangarajan, S. Ranka, et al. A general framework for error-controlled unstructured scientific data compression.In 2024 IEEE 20th International Conference on e-Science (e-Science), pages 1–10. IEEE, 2024.
G. Hari, N. Joshi, Z. Wang, Q. Gong, D. Pugmire, K. Moreland, C. R. Johnson, S. Klasky, N. Pod-
horszki, and T. M. Athawale. FunM2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices. In 2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, pages 43–47. IEEE, 2024.
R. Jain, H. Tang, A. Dhruv, and S. Byna. Enabling Data Reduction for Flash-X Simulations. In
SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 270–279, 2024.
H. Jin, P. Balaprakash, A. Zou, P. Ghysels, A. S. Krishnapriyan, A. Mate, A. Barnes, and R. Bent. Physics-informed heterogeneous graph neural networks for DC blocker placement. Electric Power Systems Research, 235:110795, 2024.
Kwok, Ka Hei Martin, Kortelainen, Matti, Cerati, Giuseppe, Strelchenko, Alexei, Gutsche, Oliver, Reinsvold Hall, Allison, Lantz, Steve, Reid, Michael, Riley, Daniel, Berkman, Sophie, Lee, Seyong, Ather, Hammad, Norris, Boyana, and Wang, Cong. Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels. EPJ Web of Conf., 295:11003, 2024.
X. Li, Q. Gong, J. Lee, S. Klasky, A. Rangarajan, and S. Ranka. Hybrid approaches for data reduction of spatiotemporal scientific applications. In 2024 Data Compression Conference (DCC), pages 567–567. IEEE, 2024.
X. Li, J. Lee, A. Rangarajan, and S. Ranka. Attention based machine learning methods for data reduction with guaranteed error bounds. In 2024 IEEE International Conference on Big Data (BigData), pages 1039–1048. IEEE, 2024.
Z. Li, X. Wang, H.-Y. Chen, H.-W. Shen, and W.-L. H. Chao. FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in Neural Information Processing Systems, volume 37, pages 133948–133974. Curran Associates, Inc., 2024.
X. Luo, S. Lurvey, Y. Huang, Y. Ren, J. Huang, and B.-J. Yoon. Efficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling. In Workshop on Machine Learning and Compression, NeurIPS 2024, 2024.
X. Luo, X. Qian, and B.-J. Yoon. Hierarchical Neural Operator Transformer with Learnable
Frequency-aware Loss Prior for Arbitrary-scale Super-resolution. In Forty-first International Con-
ference on Machine Learning, 2024.
X. Luo, W. Xu, B. Nadiga, Y. Ren, and S. Yoo. Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks. In The Twelfth International Conference on Learning
Representations, 2024.
Z. Luo and S. F. Siegel. Collective Contracts for Message-Passing Parallel Programs. In A. Gurfinkel and V. Ganesh, editors, Computer Aided Verification (CAV 2024), volume 14682 of Lecture Notes in Computer Science, pages 44–68, Cham, 2024.
T. Mallick, O. Yildiz, D. Lenz, and T. Peterka. ChatVis: Automating Scientific Visualization with
a Large Language Model. In SC24-W: Workshops of the International Conference for High Perfor-
mance Computing, Networking, Storage and Analysis, pages 49–55. IEEE, 2024.
K. Mehta, M. Lupo Pasini, S. Irle, P. Yoo, F. Suter, D. Ganyushin, and S. Klasky. Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 05 2024.
U. Mukhopadhyay, A. Tripathy, O. Selvitopi, K. Yelick, and A. Buluc¸. Sparsity-Aware Commu-
nication for Distributed Graph Neural Network Training. In Proceedings of the 53rd International
Conference on Parallel Processing, pages 117–126, 2024.
I. Naiman, N. B. Erichson, P. Ren, M. W. Mahoney, and O. Azencot. Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs. In The Twelfth International Conference on Learning Representations, 2024.
V. Nilsson, A. Samaddar, S. Madireddy, and P. Nyquist. REMEDI: Corrective Transformations for Improved Neural Entropy Estimation. In R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, . Scarlett, and F. Berkenkamp, editors, Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pages 38207–38236. PMLR, 21–27 Jul 2024.
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J. L. Bez, H. Ather, and S. Byna. Drishti: Guiding End-Users in the I/O Optimization Journey. In 2022 IEEE/ACM International Parallel Data Systems Workshop (PDSW), pages 1–6, 2022.
M. Cianciosa, R. Archibald, W. Elwasif, A. Gainaru, J. M. Park, and R. Whitfield. Adaptive Generation of Training Data for ML Reduced Model Creation. In 2022 IEEE International Conference on Big Data (Big Data), pages 3408–3416, 2022.
A. P. Dieguez, M. Choi, X. Zhu, B. M. Wong, and K. Z. Ibrahim. ML-based Performance Portability for Time-Dependent Density Functional Theory in HPC Environments. In IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, PMBS@SC 2022, Dallas, TX, USA, November 13-18, 2022, pages 1–12. IEEE, 2022.
R. Egele, R. Maulik, K. Raghavan, B. Lusch, I. Guyon, and P. Balaprakash. Autodeuq: Automated deep ensemble with uncertainty quantification. In 2022 26th International Conference on Pattern Recognition (ICPR), pages 1908–1914. IEEE, 2022.
N. A. Garland, R. Maulik, Q. Tang, X.-Z. Tang, and P. Balaprakash. Efficient data acquisition and
training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling. Machine learning: science and technology, 3(4):045003, 2022.
G. Georgakoudis, T. R. Scogland, C. Liao, and B. R. de Supinski. Extending OpenMP to Support Automated Function Specialization Across Translation Units. In International Workshop on OpenMP, pages 159–173. Springer, 2022.
Q. Gong, B. Whitney, C. Zhang, X. Liang, A. Rangarajan, J. Chen, L. Wan, P. Ullrich, Q. Liu, R. Jacob, et al. Region-adaptive, error-controlled scientific data compression using multilevel decomposition. In Proceedings of the 34th International Conference on Scientific and Statistical Database Management, pages 1–12, 2022.
J. Gu, G. Eisenhauer, S. Klasky, N. Podhorszki, R. Wang, and K. Wu. Exploring Large All-Flash
Storage System with Scientific Simulation. In E. Pourabbas, Y. Zhou, Y. Li, and B. Yang, editors,
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Copenhagen, Denmark, July 6 - 8, 2022, pages 23:1–23:4. ACM, 2022.
R. Han, S. Byna, H. Tang, B. Dong, and M. Zheng. PROV-IO: An I/O-Centric Provenance Framework for Scientific Data on HPC Systems. In Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing, HPDC ’22, page 213–226, New York, NY, USA,2022.
K. Huck, X. Wu, A. Dubey, A. Georgiadou, J. A. Harris, T. Klosterman, M. Trappett, and K. Weide.
Performance Debugging and Tuning of Flash-X with Data Analysis Tools. In 2022 IEEE/ACM Workshop on Programming and Performance Visualization Tools (ProTools), pages 1–10. IEEE, 2022.
K. A. Huck. Broad performance measurement support for asynchronous multi-tasking with apex. In 2022 IEEE/ACM 7th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2), pages 20–29. IEEE, 2022.
K. Z. Ibrahim and L. Oliker. Preprocessing Pipeline Optimization for Scientific Deep Learning Workloads. In 2022 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2022, Lyon, France, May 30 - June 3, 2022, pages 1118–1128. IEEE, 2022.
K. Z. Ibrahim, C. Yang, and P. Maris. Performance Portability of Sparse Block Diagonal Matrix Multiple Vector Multiplications on GPUs. In IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC, P3HPC@SC 2022, Dallas, TX, USA, November 13-18, 2022, pages 58–67. IEEE, 2022.
M. Isakov, M. Currier, E. Del Rosario, S. Madireddy, P. Balaprakash, P. Carns, R. B. Ross, G. K. Lockwood, and M. A. Kinsy. A Taxonomy of Error Sources in HPC I/O Machine Learning Models. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, International Conference for High Performance Computing, Networking, Storage, and Analysis SC’22, 2022.
R. Krishnan and P. Balaprakash. Continual Learning via Dynamic Programming. International Conference on Pattern Recognition (ICPR), pages 1350–1356. IEEE, 2022.
In 2022 26th
J. Lambert, M. A. H. Monil, S. Lee, A. D. Malony, and J. S. Vetter. Leveraging Compiler-Based Translation to Evaluate a Diversity of Exascale Platforms. In 2022 IEEE/ACM International Work-
shop on Performance, Portability and Productivity in HPC (P3HPC), pages 14–25, 2022.
K. Mehta, A. Cliff, F. Suter, A. Walker, M. Wolf, D. Jacobson, and S. Klasky. Running Ensemble Workflows at Extreme Scale: Lessons Learned and Path Forward. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 10 2022.
M. A. H. Monil, S. Lee, J. S. Vetter, and A. D. Malony. MAPredict: Static Analysis Driven Memory Access Prediction Framework for Modern CPUs. In A.-L. Varbanescu, A. Bhatele, P. Luszczek, and B. Marc, editors, High Performance Computing, pages 233–255, Cham,Springer International Publishing. 2022.
K. Moreland, A. C. Bauer, B. Geveci, P. O’Leary, and B. Whitlock. Leveraging Production Visualization Tools In Situ. In H. Childs, J. C. Bennett, and C. Garth, editors, In Situ Visualization for Computational Science, pages 205–231. Springer, 2022.
A. Nigmetov and D. Morozov. Fast Merge Tree Computation via SYCL. In 2022 Topological Data Analysis and Visualization (TopoInVis), pages 1–8. IEEE, 2022.
F. Poeschel, J. E, W. F. Godoy, N. Podhorszki, S. Klasky, G. Eisenhauer, P. E. Davis, L. Wan,
A. Gainaru, J. Gu, F. Koller, R. Widera, M. Bussmann, and A. Huebl. Transitioning from File-Based HPC Workflows to Streaming Data Pipelines with openPMD and ADIOS2. In Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling andSimulation, pages 99–118, Cham, 2022.
S. Premchandar, S. R. Jantre, P. Balaprakash, and S. Madireddy. Unified probabilistic neural architecture and weight ensembling improves model robustness. Machine Learning Safety Workshopat 36th Conference on Neural Information Processing Systems (NeurIPS). 2022.
D. Pugmire, J. Huang, K. Moreland, and S. Klasky. The Need for Pervasive In Situ Analysis and Visualization (P-ISAV). In H. Anzt, A. Bienz, P. Luszczek, and M. Baboulin, editors, High Performance Computing. ISC High Performance 2022 International Workshops, pages 306–316,Cham. Springer International Publishing.2022
R. A. Sinurat, A. Daram, H. S. Gunawi, R. B. Ross, and S. Madireddy. Towards Continually Learning Application Performance Models. 2022.
E. Suchyta, J. Y. Choi, S.-H. Ku, D. Pugmire, A. Gainaru, K. Huck, R. Kube, A. Scheinberg, F. Suter, C. Chang, T. Munson, N. Podhorszki, and S. Klasky. Hybrid Analysis of Fusion Data for Online Understanding of Complex Science on Extreme Scale Computers. In 2022 IEEE International Conference on Cluster Computing (CLUSTER), pages 218–229, 2022.
P. Valero-Lara, S. Lee, M. Gonzalez-Tallada, J. Denny, and J. S. Vetter. KokkACC: Enhancing Kokkos with OpenACC. In 2022 Workshop on Accelerator Programming Using Directives (WACCPD), pages 32–42, 2022.
J. Wang, P. Grosset, T. L. Turton, and J. Ahrens. Analyzing the Impact of Lossy Data Reduction on Volume Rendering of Cosmology Data. In 2022 IEEE/ACM 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD), pages 11–20, 2022.
W. Wu, J. H uckelheim, P. D. Hovland, and S. F. Siegel. Verifying Fortran Programs with CIVL. In D. Fisman and G. Rosu, editors, Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2022), volume 13243 of Lecture Notes in Computer Science, pages 106–124, Cham, 2022.
O. Yildiz, H. Chan, K. Raghavan, W. Judge, M. J. Cherukara, P. Balaprakash, S. Sankaranarayanan, and T. Peterka. Automated continual learning of defect identification in coherent diffraction imag ing. In 2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S), pages 1–6. IEEE, 2022.
2021
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
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
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
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
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
Posters
2018
2018
Jungwon Kim, Jeffrey S. Vetter, "Implementing Efficient Data Compression and Encryption in a Persistent Key-Value Store for HPC", poster in International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18).
Philip C. Roth, Sophie Blondel, David E. Bernholdt, Brian D. Wirth, "Improving the I/O Performance and Memory Usage of the Xolotl Cluster Dynamics Simulator", poster in International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18).
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