Brian Van Essen

43 papers A 9B 7C 2Misc 2Journal 15Unranked 8
YearRankTypeTitle / Venue / Authors
2025 J jnl
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models.
CoRR
Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun, Peggy Li, James Diffenderfer, Busra Demirci, Obadiah Smolenski, Tim Hsu, Anna M. Hiszpanski, Kenneth Chiu, Bhavya Kailkhura, Brian Van Essen
2024 J jnl
Lion Cub: Minimizing Communication Overhead in Distributed Lion.
CoRR
Satoki Ishikawa, Tal Ben-Nun, Brian Van Essen, Rio Yokota, Nikoli Dryden
2022 J jnl
Enabling machine learning-ready HPC ensembles with Merlin.
Future Gener. Comput. Syst.
J. Luc Peterson, Benjamin Bay, Joe Koning, Peter B. Robinson, Jessica Semler, Jeremy White, Rushil Anirudh, Kevin Athey, Peer-Timo Bremer, Francesco Di Natale, David Fox, Jim A. Gaffney, Sam Ade Jacobs, Bhavya Kailkhura, Bogdan Kustowski, Steve H. Langer, Brian K. Spears, Jayaraman J. Thiagarajan, Brian Van Essen, Jae-Seung Yeom
2022 B conf
Parallelizing Graph Neural Networks via Matrix Compaction for Edge-Conditioned Networks.
CCGRID
Shehtab Zaman, Tim Moon, Tom Benson, Sam Adé Jacobs, Kenneth Chiu, Brian Van Essen
2022 B conf
Scalable Composition and Analysis Techniques for Massive Scientific Workflows.
e-Science
Dong H. Ahn, Xiaohua Zhang, Jeffrey Mast, Stephen Herbein, Francesco Di Natale, Dan Kirshner, Sam Ade Jacobs, Ian Karlin, Daniel J. Milroy, Bronis R. de Supinski, Brian Van Essen, Jonathan E. Allen, Felice C. Lightstone
2021 J jnl
Co-design Center for Exascale Machine Learning Technologies (ExaLearn).
Int. J. High Perform. Comput. Appl.
Francis J. Alexander, James Ang, Jenna A. Bilbrey, Jan Balewski, Tiernan Casey, Ryan Chard, Jong Choi, Sutanay Choudhury, Bert J. Debusschere, Anthony M. DeGennaro, Nikoli Dryden, J. Austin Ellis, Ian T. Foster, Cristina Garcia-Cardona, Sayan Ghosh, Peter Harrington, Yunzhi Huang, Shantenu Jha, Travis Johnston, Ai Kagawa, Ramakrishnan Kannan, Neeraj Kumar, Zhengchun Liu, Naoya Maruyama, Satoshi Matsuoka, Erin McCarthy, Jamaludin Mohd-Yusof, Peter Nugent, Yosuke Oyama, Thomas Proffen, David Pugmire, Sivasankaran Rajamanickam, Vinay Ramakrishnaiah, Malachi Schram, Sudip K. Seal, Ganesh Sivaraman, Christine Sweeney, Li Tan, Rajeev Thakur, Brian Van Essen, Logan T. Ward, Paul M. Welch, Michael Wolf, Sotiris S. Xantheas, Kevin G. Yager, Shinjae Yoo, Byung-Jun Yoon
2021 J jnl
Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models.
Int. J. High Perform. Comput. Appl.
Sam Ade Jacobs, Tim Moon, Kevin McLoughlin, Derek Jones, David Hysom, Dong H. Ahn, John C. Gyllenhaal, Pythagoras Watson, Felice C. Lightstone, Jonathan E. Allen, Ian Karlin, Brian Van Essen
2021 — conf
Is Disaggregation possible for HPC Cognitive Simulation?
MLHPC@SC
Michael R. Wyatt II, Valen Yamamoto, Zoë Tosi, Ian Karlin, Brian Van Essen
2021 J jnl
Is Disaggregation possible for HPC Cognitive Simulation?
CoRR
Michael R. Wyatt II, Valen Yamamoto, Zoë Tosi, Ian Karlin, Brian Van Essen
2021 J jnl
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations.
Nat. Mach. Intell.
Harsh Bhatia, Timothy S. Carpenter, Helgi I. Ingólfsson, Gautham Dharuman, Piyush Karande, Shusen Liu, Tomas Oppelstrup, Chris Neale, Felice C. Lightstone, Brian Van Essen, James N. Glosli, Peer-Timo Bremer
2021 C conf
Monitoring Large Scale Supercomputers: A Case Study with the Lassen Supercomputer.
CLUSTER
Tapasya Patki, Adam Bertsch, Ian Karlin, Dong H. Ahn, Brian Van Essen, Barry Rountree, Bronis R. de Supinski, Nathan Besaw
2021 A conf
SUPER: SUb-Graph Parallelism for TransformERs.
IPDPS
Arpan Jain, Tim Moon, Tom Benson, Hari Subramoni, Sam Adé Jacobs, Dhabaleswar K. Panda, Brian Van Essen
2021 J jnl
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism.
IEEE Trans. Parallel Distributed Syst.
Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen
2020 J jnl
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism.
CoRR
Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen
2019 A conf
Channel and filter parallelism for large-scale CNN training.
SC
Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen
2019 A conf
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism.
IPDPS
Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen
2019 J jnl
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism.
CoRR
Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen
2019 J jnl
Merlin: Enabling Machine Learning-Ready HPC Ensembles.
CoRR
J. Luc Peterson, Rushil Anirudh, Kevin Athey, Benjamin Bay, Peer-Timo Bremer, Vic Castillo, Francesco Di Natale, David Fox, Jim A. Gaffney, David Hysom, Sam Ade Jacobs, Bhavya Kailkhura, Joe Koning, Bogdan Kustowski, Steven H. Langer, Peter B. Robinson, Jessica Semler, Brian K. Spears, Jayaraman J. Thiagarajan, Brian Van Essen, Jae-Seung Yeom
2019 C conf
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets.
CLUSTER
Sam Ade Jacobs, Jim Gaffney, Tom Benson, Peter B. Robinson, J. Luc Peterson, Brian K. Spears, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer
2019 J jnl
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets.
CoRR
Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter B. Robinson, J. Luc Peterson, Brian K. Spears
2019 A conf
Preparation and optimization of a diverse workload for a large-scale heterogeneous system.
SC
Ian Karlin, Yoonho Park, Bronis R. de Supinski, Peng Wang, Bert Still, David Beckingsale, Robert Blake, Tong Chen, Guojing Cong, Carlos H. A. Costa, Johann Dahm, Giacomo Domeniconi, Thomas Epperly, Aaron Fisher, Sara Kokkila Schumacher, Steven H. Langer, Hai Le, Eun Kyung Lee, Naoya Maruyama, Xinyu Que, David F. Richards, Björn Sjögreen, Jonathan Wong, Carol S. Woodward, Ulrike Meier Yang, Xiaohua Zhang, Bob Anderson, David Appelhans, Levi Barnes, Peter D. Barnes Jr., Sorin Bastea, David Böhme, Jamie A. Bramwell, James M. Brase, José R. Brunheroto, Barry Chen, Charway R. Cooper, Tony Degroot, Robert D. Falgout, Todd Gamblin, David J. Gardner, James N. Glosli, John A. Gunnels, Max P. Katz, Tzanio V. Kolev, I-Feng W. Kuo, Matthew P. LeGendre, Ruipeng Li, Pei-Hung Lin, Shelby Lockhart, Kathleen McCandless, Claudia Misale, Jaime H. Moreno, Rob Neely, Jarom Nelson, Rao Nimmakayala, Kathryn M. O'Brien, Kevin O'Brien, Ramesh Pankajakshan, Roger Pearce, Slaven Peles, Phil Regier, Steven C. Rennich, Martin Schulz, Howard Scott, James C. Sexton, Kathleen Shoga, Shiv Sundram, Guillaume Thomas-Collignon, Brian Van Essen, Alexey Voronin, Bob Walkup, Lu Wang, Chris Ward, Hui-Fang Wen, Daniel A. White, Christopher Young, Cyril Zeller, Edward Zywicz
2018 J jnl
CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research.
BMC Bioinform.
Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson T. Collier, John Bauer, Fangfang Xia, Thomas S. Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia-Cardona, Brian Van Essen, Matthew Baughman
2017 B conf
Convolutional sparse coding on neurosynaptic cognitive system.
IJCNN
Md. Zahangir Alom, Brian Van Essen, Adam T. Moody, David Peter Widemann, Tarek M. Taha
2017 B conf
Quadratic Unconstrained Binary Optimization (QUBO) on neuromorphic computing system.
IJCNN
Md. Zahangir Alom, Brian Van Essen, Adam T. Moody, David Peter Widemann, Tarek M. Taha
2017 — conf
Towards Scalable Parallel Training of Deep Neural Networks.
MLHPC@SC
Sam Ade Jacobs, Nikoli Dryden, Roger A. Pearce, Brian Van Essen
2016 — conf
Communication Quantization for Data-Parallel Training of Deep Neural Networks.
MLHPC@SC
Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen
2016 — conf
Towards a Distributed Large-Scale Dynamic Graph Data Store.
IPDPS Workshops
Keita Iwabuchi, Scott Sallinen, Roger A. Pearce, Brian Van Essen, Maya B. Gokhale, Satoshi Matsuoka
2016 A conf
Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications.
SC
Jun Sawada, Filipp Akopyan, Andrew S. Cassidy, Brian Taba, Michael V. DeBole, Pallab Datta, Rodrigo Alvarez-Icaza, Arnon Amir, John V. Arthur, Alexander Andreopoulos, Rathinakumar Appuswamy, Heinz Baier, Davis Barch, David J. Berg, Carmelo di Nolfo, Steven K. Esser, Myron Flickner, Thomas A. Horvath, Bryan L. Jackson, Jeff Kusnitz, Scott Lekuch, Michael Mastro, Timothy Melano, Paul A. Merolla, Steven E. Millman, Tapan K. Nayak, Norm Pass, Hartmut E. Penner, William P. Risk, Kai Schleupen, Benjamin G. Shaw, Hayley Wu, Brian Giera, Adam T. Moody, T. Nathan Mundhenk, Brian Van Essen, Eric X. Wang, David P. Widemann, Qing Wu, William E. Murphy, Jamie K. Infantolino, James A. Ross, Dale R. Shires, Manuel M. Vindiola, Raju Namburu, Dharmendra S. Modha
2015 B conf
A Container-Based Approach to OS Specialization for Exascale Computing.
IC2E
Judicael A. Zounmevo, Swann Perarnau, Kamil Iskra, Kazutomo Yoshii, Roberto Gioiosa, Brian Van Essen, Maya B. Gokhale, Edgar A. León
2015 J jnl
DI-MMAP - a scalable memory-map runtime for out-of-core data-intensive applications.
Clust. Comput.
Brian Van Essen, Henry Hsieh, Sasha Ames, Roger A. Pearce, Maya B. Gokhale
2015 — conf
LBANN: livermore big artificial neural network HPC toolkit.
MLHPC@SC
Brian Van Essen, Hyojin Kim, Roger A. Pearce, Kofi Boakye, Barry Chen
2015 J jnl
Large-Scale Deep Learning on the YFCC100M Dataset.
CoRR
Karl Ni, Roger A. Pearce, Kofi Boakye, Brian Van Essen, Damian Borth, Barry Chen, Eric X. Wang
2014 — conf
Multi-threaded streamline tracing for data-intensive architectures.
LDAV
Ming Jiang, Brian Van Essen, Cyrus Harrison, Maya B. Gokhale
2012 Misc conf
Accelerating a Random Forest Classifier: Multi-Core, GP-GPU, or FPGA?
FCCM
Brian Van Essen, Chris Macaraeg, Maya B. Gokhale, Ryan Prenger
2012 — conf
DI-MMAP: A High Performance Memory-Map Runtime for Data-Intensive Applications.
SC Companion
Brian Van Essen, Henry Hsieh, Sasha Ames, Maya B. Gokhale
2012 — conf
Integrated in-system storage architecture for high performance computing.
ROSS@ICS
Dries Kimpe, Kathryn M. Mohror, Adam Moody, Brian Van Essen, Maya B. Gokhale, Robert B. Ross, Bronis R. de Supinski
2012 A conf
On the Role of NVRAM in Data-intensive Architectures: An Evaluation.
IPDPS
Brian Van Essen, Roger A. Pearce, Sasha Ames, Maya B. Gokhale
2011 A conf
Energy-efficient specialization of functional units in a coarse-grained reconfigurable array.
FPGA
Brian Van Essen, Robin Panda, Aaron Wood, Carl Ebeling, Scott Hauck
2010 B conf
Managing Short-Lived and Long-Lived Values in Coarse-Grained Reconfigurable Arrays.
FPL
Brian Van Essen, Robin Panda, Aaron Wood, Carl Ebeling, Scott Hauck
2009 A conf
SPR: an architecture-adaptive CGRA mapping tool.
FPGA
Stephen Friedman, Allan Carroll, Brian Van Essen, Benjamin Ylvisaker, Carl Ebeling, Scott Hauck
2009 B conf
Static versus scheduled interconnect in Coarse-Grained Reconfigurable Arrays.
FPL
Brian Van Essen, Aaron Wood, Allan Carroll, Stephen Friedman, Robin Panda, Benjamin Ylvisaker, Carl Ebeling, Scott Hauck
2006 Misc conf
A Type Architecture for Hybrid Micro-Parallel Computers.
FCCM
Benjamin Ylvisaker, Brian Van Essen, Carl Ebeling
2006 A conf
A type architecture for hybrid micro-parallel computers.
FPGA
Benjamin Ylvisaker, Brian Van Essen, Carl Ebeling