Brian Van Essen

Publication Activity (10 Years)

Years Active: 2006-2022
Publications (10 Years): 30

Top Topics

Discriminatively Trained

Machine Learning

Top Venues

Publications

Shehtab Zaman, Tim Moon, Tom Benson, Sam Adé Jacobs, Kenneth Chiu, Brian Van Essen
Parallelizing Graph Neural Networks via Matrix Compaction for Edge-Conditioned Networks. CCGRID (2022)
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
Scalable Composition and Analysis Techniques for Massive Scientific Workflows. e-Science (2022)
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
Enabling machine learning-ready HPC ensembles with Merlin. Future Gener. Comput. Syst. 131 (2022)
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
Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models. Int. J. High Perform. Comput. Appl. 35 (5) (2021)
Michael R. Wyatt II, Valen Yamamoto, Zoë Tosi, Ian Karlin, Brian Van Essen
Is Disaggregation possible for HPC Cognitive Simulation? CoRR (2021)
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
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations. Nat. Mach. Intell. 3 (5) (2021)
Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism. IEEE Trans. Parallel Distributed Syst. 32 (7) (2021)
Michael R. Wyatt II, Valen Yamamoto, Zoë Tosi, Ian Karlin, Brian Van Essen
Is Disaggregation possible for HPC Cognitive Simulation? MLHPC@SC (2021)
Arpan Jain, Tim Moon, Tom Benson, Hari Subramoni, Sam Adé Jacobs, Dhabaleswar K. Panda, Brian Van Essen
SUPER: SUb-Graph Parallelism for TransformERs. IPDPS (2021)
Tapasya Patki, Adam Bertsch, Ian Karlin, Dong H. Ahn, Brian Van Essen, Barry Rountree, Bronis R. de Supinski, Nathan Besaw
Monitoring Large Scale Supercomputers: A Case Study with the Lassen Supercomputer. CLUSTER (2021)
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 Ramakrishniah, 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
Co-design Center for Exascale Machine Learning Technologies (ExaLearn). Int. J. High Perform. Comput. Appl. 35 (6) (2021)
Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism. CoRR (2020)
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
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. CoRR (2019)
Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism. CoRR (2019)
Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen
Channel and filter parallelism for large-scale CNN training. SC (2019)
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
Merlin: Enabling Machine Learning-Ready HPC Ensembles. CoRR (2019)
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
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. CLUSTER (2019)
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
Preparation and optimization of a diverse workload for a large-scale heterogeneous system. SC (2019)
Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism. IPDPS (2019)
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
CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research. BMC Bioinform. (18) (2018)
Md. Zahangir Alom, Brian Van Essen, Adam T. Moody, David Peter Widemann, Tarek M. Taha
Quadratic Unconstrained Binary Optimization (QUBO) on neuromorphic computing system. IJCNN (2017)
Md. Zahangir Alom, Brian Van Essen, Adam T. Moody, David Peter Widemann, Tarek M. Taha
Convolutional sparse coding on neurosynaptic cognitive system. IJCNN (2017)
Sam Ade Jacobs, Nikoli Dryden, Roger A. Pearce, Brian Van Essen
Towards Scalable Parallel Training of Deep Neural Networks. MLHPC@SC (2017)
Keita Iwabuchi, Scott Sallinen, Roger A. Pearce, Brian Van Essen, Maya B. Gokhale, Satoshi Matsuoka
Towards a Distributed Large-Scale Dynamic Graph Data Store. IPDPS Workshops (2016)
Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen
Communication Quantization for Data-Parallel Training of Deep Neural Networks. MLHPC@SC (2016)
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 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
Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications. SC (2016)
Karl Ni, Roger A. Pearce, Kofi Boakye, Brian Van Essen, Damian Borth, Barry Chen, Eric X. Wang
Large-Scale Deep Learning on the YFCC100M Dataset. CoRR (2015)
Brian Van Essen, Henry Hsieh, Sasha Ames, Roger A. Pearce, Maya B. Gokhale
DI-MMAP - a scalable memory-map runtime for out-of-core data-intensive applications. Clust. Comput. 18 (1) (2015)
Brian Van Essen, Hyojin Kim, Roger A. Pearce, Kofi Boakye, Barry Chen
LBANN: livermore big artificial neural network HPC toolkit. MLHPC@SC (2015)
Judicael A. Zounmevo, Swann Perarnau, Kamil Iskra, Kazutomo Yoshii, Roberto Gioiosa, Brian Van Essen, Maya B. Gokhale, Edgar A. León
A Container-Based Approach to OS Specialization for Exascale Computing. IC2E (2015)
Ming Jiang, Brian Van Essen, Cyrus Harrison, Maya B. Gokhale
Multi-threaded streamline tracing for data-intensive architectures. LDAV (2014)
Dries Kimpe, Kathryn Mohror, Adam Moody, Brian Van Essen, Maya B. Gokhale, Robert B. Ross, Bronis R. de Supinski
Integrated in-system storage architecture for high performance computing. ROSS@ICS (2012)
Brian Van Essen, Roger A. Pearce, Sasha Ames, Maya B. Gokhale
On the Role of NVRAM in Data-intensive Architectures: An Evaluation. IPDPS (2012)
Brian Van Essen, Chris Macaraeg, Maya B. Gokhale, Ryan Prenger
Accelerating a Random Forest Classifier: Multi-Core, GP-GPU, or FPGA? FCCM (2012)
Brian Van Essen, Henry Hsieh, Sasha Ames, Maya B. Gokhale
DI-MMAP: A High Performance Memory-Map Runtime for Data-Intensive Applications. SC Companion (2012)
Brian Van Essen, Robin Panda, Aaron Wood, Carl Ebeling, Scott Hauck
Energy-efficient specialization of functional units in a coarse-grained reconfigurable array. FPGA (2011)
Brian Van Essen, Robin Panda, Aaron Wood, Carl Ebeling, Scott Hauck
Managing Short-Lived and Long-Lived Values in Coarse-Grained Reconfigurable Arrays. FPL (2010)
Brian Van Essen, Aaron Wood, Allan Carroll, Stephen Friedman, Robin Panda, Benjamin Ylvisaker, Carl Ebeling, Scott Hauck
Static versus scheduled interconnect in Coarse-Grained Reconfigurable Arrays. FPL (2009)
Stephen Friedman, Allan Carroll, Brian Van Essen, Benjamin Ylvisaker, Carl Ebeling, Scott Hauck
SPR: an architecture-adaptive CGRA mapping tool. FPGA (2009)
Benjamin Ylvisaker, Brian Van Essen, Carl Ebeling
A type architecture for hybrid micro-parallel computers. FPGA (2006)
Benjamin Ylvisaker, Brian Van Essen, Carl Ebeling
A Type Architecture for Hybrid Micro-Parallel Computers. FCCM (2006)