Wahid Bhimji

According to our database, Wahid Bhimji authored at least 20 papers between 2011 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access.

Wahid Bhimji, Dale Carder, Eli Dart, Javier M. Duarte, Ian Fisk, Robert W. Gardner, Chin Guok, Bo Jayatilaka, Tom Lehman, M. Lin, Carlos Maltzahn, Shawn McKee, Mark S. Neubauer, O. Rind, Oksana Shadura, N. V. Tran, P. van Gemmeren, Gordon Watts, B. A. Weaver, Frank Würthwein

Comput. Softw. Big Sci., December, 2023

High-level GPU code: a case study examining JAX and OpenMP.

Nestor Demeure, Theodore Kisner, Reijo Keskitalo, Rollin C. Thomas, Julian Borrill, Wahid Bhimji

Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior.

Shashank Subramanian, Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence.

Ashesh Chattopadhyay, Jaideep Pathak, Ebrahim Nabizadeh, Wahid Bhimji, Pedram Hassanzadeh

CoRR, 2022

2021

The Petascale DTN Project: High Performance Data Transfer for HPC Facilities.

Eli Dart, William E. Allcock, Wahid Bhimji, Tim Boerner, Ravinderjeet Cheema, Andrew Cherry, Brent Draney, Salman Habib, Damian Hazen, Jason Hill, Matt Kollross, Suzanne Parete-Koon, Daniel Pelfrey, Adrian Pope, Jeff Porter, David Wheeler

CoRR, 2021

Architectural Requirements for Deep Learning Workloads in HPC Environments.

Khaled Z. Ibrahim, Tan Nguyen, Hai Ah Nam, Wahid Bhimji, Steven Farrell, Leonid Oliker, Michael Rowan, Nicholas J. Wright, Samuel Williams

Proceedings of the 2021 International Workshop on Performance Modeling, 2021

2020

A Pattern Recognition Algorithm for Quantum Annealers.

Frédéric Bapst, Wahid Bhimji, Paolo Calafiura, Heather M. Gray, Wim Lavrijsen, Lucy Linder, Alex Smith

Comput. Softw. Big Sci., December, 2020

2019

Etalumis: bringing probabilistic programming to scientific simulators at scale.

Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas 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

Proceedings of the International Conference for High Performance Computing, 2019

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.

Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas 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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.

Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood

CoRR, 2018

Machine Learning in High Energy Physics Community White Paper.

Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier M. Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir V. Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemyslaw Karpinski, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark S. Neubauer, Harvey B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Chatham Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata

CoRR, 2018

Interactive Distributed Deep Learning with Jupyter Notebooks.

Steven Andrew Farrell, Aaron Vose, Oliver Evans, Matthew L. Henderson, Shreyas Cholia, Fernando Pérez, Wahid Bhimji, Shane Canon, Rollin C. Thomas, Prabhat

Proceedings of the High Performance Computing, 2018

Graph Neural Networks for IceCube Signal Classification.

Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna

Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018

2017

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators.

Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat

CoRR, 2017

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC.

Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah

CoRR, 2017

Creating Virtual Universes Using Generative Adversarial Networks.

Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Rami Al-Rfou, Zarija Lukic

CoRR, 2017

Deep learning at 15PF: supervised and semi-supervised classification for scientific data.

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

Proceedings of the International Conference for High Performance Computing, 2017

2016

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.

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

Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium, 2016

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks.

Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat

Proceedings of the 15th IEEE International Conference on Machine Learning and Applications, 2016

2011

Establishing Applicability of SSDs to LHC Tier-2 Hardware Configuration

Samuel C. Skipsey, Wahid Bhimji, Mike Kenyon

CoRR, 2011