Prabhat

Publication Activity (10 Years)

Years Active: 2005-2021
Publications (10 Years): 75

Top Topics

Matrix Factorization

Unsupervised Segmentation

Bayesian Inference

Top Venues

Publications

Luca Pion-Tonachini, Kristofer E. Bouchard, Héctor García Martín, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko M. Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel B. Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus H. Zwart, Neeraj Kumar, Amy Justice, Claire J. Tomlin, Daniel A. Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Kenneth Kreutz-Delgado, Michael W. Mahoney, James B. Brown
Learning from learning machines: a new generation of AI technology to meet the needs of science. CoRR (2021)
Jin-Long Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. J. Comput. Phys. 406 (2020)
Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework. CoRR (2020)
Nicholas Choma, Daniel Murnane, Xiangyang Ju, Paolo Calafiura, Sean Conlon, Steven Farrell, Prabhat, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Panagiotis Spentzouris, Jean-Roch Vlimant, Maria Spiropulu, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy L. Usher
Track Seeding and Labelling with Embedded-space Graph Neural Networks. CoRR (2020)
Grzegorz Muszynski, Prabhat, Jan Balewski, Karthik Kashinath, Michael F. Wehner, Vitaliy Kurlin
Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data. ICPR (2020)
Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework. SC (2020)
Mahesh Balasubramanian, Trevor D. Ruiz, Brandon Cook, Prabhat, Sharmodeep Bhattacharyya, Aviral Shrivastava, Kristofer E. Bouchard
Scaling of Union of Intersections for Inference of Granger Causal Networks from Observational Data. IPDPS (2020)
Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Alexander Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. CoRR (2019)
Adam Rupe, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor W. Lee, Prabhat, James P. Crutchfield
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. CoRR (2019)
Chao Jiang, Dave Ojika, Thorsten Kurth, Prabhat, Sofia Vallecorsa, Bhavesh Patel, Herman Lam
FPGAs. ISC Workshops (2019)
Markus Reichstein, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, Nuno Carvalhais, Prabhat
Deep learning and process understanding for data-driven Earth system science. Nat. 566 (7743) (2019)
Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Alexander Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood
Etalumis: bringing probabilistic programming to scientific simulators at scale. SC (2019)
Liu Yang, Prabhat, George E. Karniadakis, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston
Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. DLS@SC (2019)
Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Jey Kottalam, Lisa Gerhardt, Prabhat, Michael F. Ringenburg, Kristyn J. Maschhoff
Alchemist: An Apache Spark ⇔ MPI interface. Concurr. Comput. Pract. Exp. 31 (16) (2019)
Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner
Spherical CNNs on Unstructured Grids. ICLR (Poster) (2019)
Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston, Prabhat, George E. Karniadakis
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR (2019)
Jeffrey Regier, Keno Fischer, Kiran Pamnany, Andreas Noack, Jarrett Revels, Maximilian Lam, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
Cataloging the visible universe through Bayesian inference in Julia at petascale. J. Parallel Distributed Comput. 127 (2019)
Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor W. Lee, Prabhat, James P. Crutchfield
Towards Unsupervised Segmentation of Extreme Weather Events. CoRR (2019)
Sookyung Kim, Sunghyun Park, Sunghyo Chung, Joonseok Lee, Yunsung Lee, Hyojin Kim, Prabhat, Jaegul Choo
Learning to Focus and Track Extreme Climate Events. BMVC (2019)
Adam Rupe, Prabhat, James P. Crutchfield, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor W. Lee
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. MLHPC@SC (2019)
Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner
Spherical CNNs on Unstructured Grids. CoRR (2019)
Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Alexander Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. NeurIPS (2019)
Babak Behzad, Surendra Byna, Prabhat, Marc Snir
Optimizing I/O Performance of HPC Applications with Autotuning. ACM Trans. Parallel Comput. 5 (4) (2019)
Sookyung Kim, Hyojin Kim, Joonseok Lee, Sangwoong Yoon, Samira Ebrahimi Kahou, Karthik Kashinath, Prabhat
Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events. WACV (2019)
Jialin Liu, Quincey Koziol, Gregory F. Butler, Neil Fortner, Mohamad Chaarawi, Houjun Tang, Suren Byna, Glenn K. Lockwood, Ravi Cheema, Kristy A. Kallback-Rose, Damian Hazen, Prabhat
Evaluation of HPC Application I/O on Object Storage Systems. PDSW-DISCS@SC (2018)
Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Diana Moise, Simon J. Pennycook, Kristyn J. Maschhoff, Jason Sewall, Nalini Kumar, Shirley Ho, Michael F. Ringenburg, Prabhat, Victor W. Lee
CosmoFlow: Using Deep Learning to Learn the Universe at Scale. CoRR (2018)
Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett H. Phillips, Ankur Mahesh, Michael A. Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston
Exascale deep learning for climate analytics. SC (2018)
Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. KDD (2018)
Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett H. Phillips, Ankur Mahesh, Michael A. Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston
Exascale Deep Learning for Climate Analytics. CoRR (2018)
Atilim Gunes Baydin, Lukas Alexander Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. CoRR (2018)
Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna
Graph Neural Networks for IceCube Signal Classification. ICMLA (2018)
Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
Cataloging the Visible Universe through Bayesian Inference at Petascale. CoRR (2018)
Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Kärnä, Diana Moise, Simon J. Pennycook, Kristyn J. Maschhoff, Jason Sewall, Nalini Kumar, Shirley Ho, Michael F. Ringenburg, Prabhat, Victor W. Lee
CosmoFlow: using deep learning to learn the universe at scale. SC (2018)
Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. CoRR (2018)
Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna
Graph Neural Networks for IceCube Signal Classification. CoRR (2018)
Steven Andrew Farrell, Aaron Vose, Oliver Evans, Matthew L. Henderson, Shreyas Cholia, Fernando Pérez, Wahid Bhimji, Shane Canon, Rollin C. Thomas, Prabhat
Interactive Distributed Deep Learning with Jupyter Notebooks. ISC Workshops (2018)
Kristofer E. Bouchard, James B. Aimone, Miyoung Chun, Thomas Dean, Michael Denker, Markus Diesmann, David Donofrio, Loren M. Frank, Narayanan Kasthuri, Christof Koch, Oliver Rübel, Horst D. Simon, Friedrich T. Sommer, Prabhat
International Neuroscience Initiatives through the Lens of High-Performance Computing. Computer 51 (4) (2018)
Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
Cataloging the Visible Universe Through Bayesian Inference at Petascale. IPDPS (2018)
Mahesh Balasubramanian, Trevor D. Ruiz, Brandon Cook, Sharmodeep Bhattacharyya, Prabhat, Aviral Shrivastava, Kristofer E. Bouchard
) Algorithms for Improved Statistical Estimation at Scale. CoRR (2018)
Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat
Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR (2018)
Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Jey Kottalam, Lisa Gerhardt, Prabhat, Michael F. Ringenburg, Kristyn J. Maschhoff
Alchemist: An Apache Spark MPI Interface. CoRR (2018)
Haoyuan Xing, Sofoklis Floratos, Spyros Blanas, Suren Byna, Prabhat, Kesheng Wu, Paul Brown
ArrayBridge: Interweaving Declarative Array Processing in SciDB with Imperative HDF5-Based Programs. ICDE (2018)
Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Alexander Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR (2017)
Thorsten Kurth, Jian Zhang, Nadathur Satish, Evan Racah, Ioannis Mitliagkas, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey
Deep learning at 15PF: supervised and semi-supervised classification for scientific data. SC (2017)
Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Chris Pal
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. NIPS (2017)
Brian Friesen, Md. Mostofa Ali Patwary, Brian Austin, Nadathur Satish, Zachary Slepian, Narayanan Sundaram, Deborah Bard, Daniel J. Eisenstein, Jack Deslippe, Pradeep Dubey, Prabhat
Galactos: computing the anisotropic 3-point correlation function for 2 billion galaxies. SC (2017)
Christopher S. Daley, Prabhat, Sudip S. Dosanjh, Nicholas J. Wright
Performance analysis of emerging data analytics and HPC workloads. PDSW-DISCS@SC (2017)
Eli Dart, Michael F. Wehner, Prabhat
An Assessment of Data Transfer Performance for Large-Scale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6. CoRR (2017)
Brian Friesen, Md. Mostofa Ali Patwary, Brian Austin, Nadathur Satish, Zachary Slepian, Narayanan Sundaram, Deborah Bard, Daniel J. Eisenstein, Jack Deslippe, Pradeep Dubey, Prabhat
Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies. CoRR (2017)
Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Prabhat, Antoine Snijders, Jian-Hua Mao, Edward F. Chang, Michael W. Mahoney, Sharmodeep Bhattacharyya
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. NIPS (2017)
Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data. CoRR (2017)
Haoyuan Xing, Sofoklis Floratos, Spyros Blanas, Suren Byna, Prabhat, Kesheng Wu, Paul Brown
ArrayBridge: Interweaving declarative array processing with high-performance computing. CoRR (2017)
Amrita Mathuriya, Thorsten Kurth, Vivek Rane, Mustafa Mustafa, Lei Shao, Debbie Bard, Prabhat, Victor W. Lee
Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer. CoRR (2017)
Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC. CoRR (2017)
Alex Gittens, Jey Kottalam, Jiyan Yang, Michael F. Ringenburg, Jatin Chhugani, Evan Racah, Mohitdeep Singh, Yushu Yao, Curt Fischer, Oliver Rübel, Benjamin P. Bowen, Norman G. Lewis, Michael W. Mahoney, Venkat Krishnamurthy, Prabhat
A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. IPDPS Workshops (2016)
Bin Dong, Suren Byna, Kesheng Wu, Prabhat, Hans Johansen, Jeffrey N. Johnson, Noel Keen
Data Elevator: Low-Contention Data Movement in Hierarchical Storage System. HiPC (2016)
Alex Gittens, Aditya Devarakonda, Evan Racah, Michael F. Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn J. Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat
Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. IEEE BigData (2016)
Yunjie Liu, Evan Racah, Prabhat, Joaquin Correa, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael F. Wehner, William D. Collins
Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. CoRR (2016)
Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter J. Sadowski, Evan Racah, Surendra Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. CoRR (2016)
Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter J. Sadowski, Evan Racah, Surendra Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. IPDPS (2016)
Evan Racah, Christopher Beckham, Tegan Maharaj, Prabhat, Christopher J. Pal
Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets. CoRR (2016)
Alex Gittens, Aditya Devarakonda, Evan Racah, Michael F. Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn J. Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies. CoRR (2016)
Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. ICMLA (2016)
Oliver Rübel, Max Dougherty, Prabhat, Peter Denes, David Conant, Edward F. Chang, Kristofer E. Bouchard
Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience. Frontiers Neuroinformatics 10 (2016)
Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin C. Thomas, David Schlegel, Jon McAuliffe, Prabhat
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference. CoRR (2016)
Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks. CoRR (2016)
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams
Scalable Bayesian Optimization Using Deep Neural Networks. ICML (2015)
Md. Mostofa Ali Patwary, Surendra Byna, Nadathur Rajagopalan Satish, Narayanan Sundaram, Zarija Lukic, Vadim Roytershteyn, Michael J. Anderson, Yushu Yao, Prabhat, Pradeep Dubey
BD-CATS: big data clustering at trillion particle scale. SC (2015)
Shane Snyder, Philip H. Carns, Robert Latham, Misbah Mubarak, Robert B. Ross, Christopher D. Carothers, Babak Behzad, Huong Vu Thanh Luu, Surendra Byna, Prabhat
Techniques for modeling large-scale HPC I/O workloads. PMBS@SC (2015)
Prabhat, Surendra Byna, Venkatram Vishwanath, Eli Dart, Michael F. Wehner, William D. Collins
TECA: Petascale Pattern Recognition for Climate Science. CAIP (2) (2015)
Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams
A Gaussian Process Model of Quasar Spectral Energy Distributions. NIPS (2015)
Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat
Celeste: Variational inference for a generative model of astronomical images. ICML (2015)
Babak Behzad, Surendra Byna, Stefan M. Wild, Prabhat, Marc Snir
Dynamic Model-Driven Parallel I/O Performance Tuning. CLUSTER (2015)
Babak Behzad, Surendra Byna, Prabhat, Marc Snir
Pattern-driven parallel I/O tuning. PDSW@SC (2015)
Huong Luu, Marianne Winslett, William Gropp, Robert B. Ross, Philip H. Carns, Kevin Harms, Prabhat, Surendra Byna, Yushu Yao
A Multiplatform Study of I/O Behavior on Petascale Supercomputers. HPDC (2015)
Babak Behzad, Surendra Byna, Stefan M. Wild, Prabhat, Marc Snir
Improving parallel I/O autotuning with performance modeling. HPDC (2014)
E. Wes Bethel, Prabhat, Surendra Byna, Oliver Rübel, K. John Wu, Michael F. Wehner
Why high performance visual data analytics is both relevant and difficult. Visualization and Data Analysis (2013)
Dean N. Williams, Peer-Timo Bremer, Charles M. Doutriaux, John Patchett, Sean Williams, Galen M. Shipman, Ross Miller, David Pugmire, Brian E. Smith, Chad A. Steed, E. Wes Bethel, Hank Childs, Harinarayan Krishnan, Prabhat, Michael F. Wehner, Cláudio T. Silva, Emanuele Santos, David Koop, Tommy Ellqvist, Jorge Poco, Berk Geveci, Aashish Chaudhary, Andy Bauer, Alexander Pletzer, Dave Kindig, Gerald Potter, Thomas P. Maxwell
Ultrascale Visualization of Climate Data. Computer 46 (9) (2013)
Babak Behzad, Joey Huchette, Huong Vu Thanh Luu, Ruth A. Aydt, Surendra Byna, Yushu Yao, Quincey Koziol, Prabhat
A framework for auto-tuning HDF5 applications. HPDC (2013)
Wei-Chen Chen, George Ostrouchov, David Pugmire, Prabhat, Michael F. Wehner
A Parallel EM Algorithm for Model-Based Clustering Applied to the Exploration of Large Spatio-Temporal Data. Technometrics 55 (4) (2013)
Christopher J. Paciorek, Benjamin Lipshitz, Wei Zhuo, Prabhat, Cari Kaufman, Rollin C. Thomas
Parallelizing Gaussian Process Calculations in R CoRR (2013)
Babak Behzad, Huong Vu Thanh Luu, Joseph Huchette, Surendra Byna, Prabhat, Ruth A. Aydt, Quincey Koziol, Marc Snir
Taming parallel I/O complexity with auto-tuning. SC (2013)
Surendra Byna, Jerry Chi-Yuan Chou, Oliver Rübel, Prabhat, Homa Karimabadi, William S. Daughton, Vadim Roytershteyn, E. Wes Bethel, Mark Howison, Ke-Jou Hsu, Kuan-Wu Lin, Arie Shoshani, Andrew Uselton, Kesheng Wu
Parallel I/O, analysis, and visualization of a trillion particle simulation. SC (2012)
Prabhat, Oliver Rübel, Surendra Byna, Kesheng Wu, Fuyu Li, Michael F. Wehner, Wes Bethel
TECA: A Parallel Toolkit for Extreme Climate Analysis. ICCS (2012)
Richard L. Martin, Prabhat, David Donofrio, James A. Sethian, Maciej Haranczyk
Accelerating analysis of void space in porous materials on multicore and GPU platforms. Int. J. High Perform. Comput. Appl. 26 (4) (2012)
Mehmet Balman, Eric Pouyoul, Yushu Yao, E. Wes Bethel, Burlen Loring, Prabhat, John Shalf, Alex Sim, Brian L. Tierney
Experiences with 100Gbps network applications. DICT@HPDC (2012)
Allen R. Sanderson, Brad Whitlock, Oliver Rübel, Hank Childs, Gunther H. Weber, Prabhat, Kenseng Wu
A System for Query Based Analysis and Visualization. EuroVA@EuroVis (2012)
Babak Behzad, Joey Huchette, Huong Luu, Ruth A. Aydt, Quincey Koziol, Prabhat, Surendra Byna, Mohamad Chaarawi, Yushu Yao
Abstract: Auto-Tuning of Parallel IO Parameters for HDF5 Applications. SC Companion (2012)
Jerry Chi-Yuan Chou, Mark Howison, Brian Austin, Kesheng Wu, Ji Qiang, E. Wes Bethel, Arie Shoshani, Oliver Rübel, Prabhat, Robert D. Ryne
Parallel index and query for large scale data analysis. SC (2011)
Jerry Chi-Yuan Chou, Kesheng Wu, Prabhat
FastQuery: A General Indexing and Querying System for Scientific Data. SSDBM (2011)
Wei Zhuo, Prabhat, Christopher J. Paciorek, Cari Kaufman, Wes Bethel
Parallel Kriging Analysis for Large Spatial Datasets. ICDM Workshops (2011)
Jerry Chi-Yuan Chou, Kesheng Wu, Prabhat
FastQuery: A Parallel Indexing System for Scientific Data. CLUSTER (2011)
Hank Childs, David Pugmire, Sean Ahern, Brad Whitlock, Mark Howison, Prabhat, Gunther H. Weber, E. Wes Bethel
Extreme Scaling of Production Visualization Software on Diverse Architectures. IEEE Computer Graphics and Applications 30 (3) (2010)
Oliver Rübel, Sean Ahern, E. Wes Bethel, Mark D. Biggin, Hank Childs, Estelle Cormier-Michel, Angela H. DePace, Michael B. Eisen, Charless C. Fowlkes, Cameron G. R. Geddes, Hans Hagen, Bernd Hamann, Min-Yu Huang, Soile V. E. Keränen, David W. Knowles, Cris L. Luengo Hendriks, Jitendra Malik, Jeremy S. Meredith, Peter Messmer, Prabhat
Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data. ICCS (1) (2010)
Prabhat, Andrew S. Forsberg, Michael Katzourin, Kristi Wharton, Mel Slater
A Comparative Study of Desktop, Fishtank, and Cave Systems for the Exploration of Volume Rendered Confocal Data Sets. IEEE Trans. Vis. Comput. Graph. 14 (3) (2008)
Oliver Rübel, Prabhat, Kesheng Wu, Hank Childs, Jeremy S. Meredith, Cameron G. R. Geddes, Estelle Cormier-Michel, Sean Ahern, Gunther H. Weber, Peter Messmer, Hans Hagen, Bernd Hamann, E. Wes Bethel
High performance multivariate visual data exploration for extremely large data. SC (2008)
Daniela Mayumi Ushizima, Oliver Rübel, Prabhat, Gunther H. Weber, E. Wes Bethel, Cecilia R. Aragon, Cameron G. R. Geddes, Estelle Cormier-Michel, Bernd Hamann, Peter Messmer, Hans Hagen
Automated Analysis for Detecting Beams in Laser Wakefield Simulations. ICMLA (2008)
Andrew S. Forsberg, Prabhat, Graff Haley, Andrew Bragdon, Joseph Levy, Caleb I. Fassett, David Shean, James W. Head III, Sarah Milkovich, Mark A. Duchaineau
Adviser: Immersive Field Work for Planetary Geoscientists. IEEE Computer Graphics and Applications 26 (4) (2006)
Prabhat, Samuel G. Fulcomer
Experiences in driving a cave with IBM scalable graphics engine-3 (SGE-3) prototypes. VRST (2005)