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ORCIDBrian Van Essen
Brian C. Van Essen
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- affiliation: Lawrence Livermore National Laboratory, CA, USA
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2020 – today
- 2022
[j9]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: 255-268 (2022)- [c31]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: 386-395 - [c30]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: 32-43 - 2021
- [j8]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) - [j7]Francis J. Alexander, James A. 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:
Co-design Center for Exascale Machine Learning Technologies (ExaLearn). Int. J. High Perform. Comput. Appl. 35(6): 598-616 (2021) - [j6]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): 401-409 (2021) - [j5]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): 1641-1652 (2021) - [c29]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: 468-480 - [c28]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: 629-638 - [c27]Michael R. Wyatt II, Valen Yamamoto, Zoë Tosi, Ian Karlin, Brian Van Essen:
Is Disaggregation possible for HPC Cognitive Simulation? MLHPC@SC 2021: 94-105 - [i10]Michael R. Wyatt II, Valen Yamamoto, Zoë Tosi, Ian Karlin, Brian Van Essen:
Is Disaggregation possible for HPC Cognitive Simulation? CoRR abs/2112.05216 (2021) - 2020
- [j4]Shusen Liu, Jim Gaffney, J. Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom:
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. IEEE Trans. Vis. Comput. Graph. 26(1): 291-300 (2020) - [i9]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 abs/2007.12856 (2020)
2010 – 2019
- 2019
- [c26]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: 1-10 - [c25]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: 210-220 - [c24]Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen:
Channel and filter parallelism for large-scale CNN training. SC 2019: 10:1-10:20 - [c23]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: 32:1-32:17 - [p1]Swann Perarnau, Brian C. Van Essen, Roberto Gioiosa, Kamil Iskra, Maya B. Gokhale, Kazutomo Yoshii, Peter H. Beckman:
Argo. Operating Systems for Supercomputers and High Performance Computing 2019: 199-220 - [i8]Ya Ju Fan, Jonathan E. Allen, Sam Ade Jacobs, Brian C. Van Essen:
Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency. CoRR abs/1901.11152 (2019) - [i7]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 abs/1903.06681 (2019) - [i6]Shusen Liu, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom, Jim Gaffney, J. Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer:
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. CoRR abs/1907.08325 (2019) - [i5]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 abs/1910.02270 (2019) - [i4]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 abs/1912.02892 (2019) - 2018
- [j3]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. 19-S(18): 59-69 (2018) - [j2]Amar Shrestha, Khadeer Ahmed, Yanzhi Wang, David P. Widemann, Adam T. Moody, Brian C. Van Essen, Qinru Qiu:
Modular Spiking Neural Circuits for Mapping Long Short-Term Memory on a Neurosynaptic Processor. IEEE J. Emerg. Sel. Topics Circuits Syst. 8(4): 782-795 (2018) - [c22]Md. Zahangir Alom, Adam T. Moody, Naoya Maruyama, Brian C. Van Essen, Tarek M. Taha:
Effective Quantization Approaches for Recurrent Neural Networks. IJCNN 2018: 1-8 - [i3]Md. Zahangir Alom, Adam T. Moody, Naoya Maruyama, Brian C. Van Essen, Tarek M. Taha:
Effective Quantization Approaches for Recurrent Neural Networks. CoRR abs/1802.02615 (2018) - [i2]Md. Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari:
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches. CoRR abs/1803.01164 (2018) - 2017
- [c21]Amar Shrestha, Khadeer Ahmed, Yanzhi Wang, David P. Widemann, Adam T. Moody, Brian C. Van Essen, Qinru Qiu:
A spike-based long short-term memory on a neurosynaptic processor. ICCAD 2017: 631-637 - [c20]Md. Zahangir Alom, Brian Van Essen, Adam T. Moody, David Peter Widemann, Tarek M. Taha:
Convolutional sparse coding on neurosynaptic cognitive system. IJCNN 2017: 3609-3616 - [c19]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: 3922-3929 - [c18]Swann Perarnau, Judicael A. Zounmevo, Matthieu Dreher, Brian C. Van Essen, Roberto Gioiosa, Kamil Iskra, Maya B. Gokhale, Kazutomo Yoshii, Peter H. Beckman:
Argo NodeOS: Toward Unified Resource Management for Exascale. IPDPS 2017: 153-162 - [c17]Sam Ade Jacobs, Nikoli Dryden, Roger A. Pearce, Brian Van Essen:
Towards Scalable Parallel Training of Deep Neural Networks. MLHPC@SC 2017: 5:1-5:9 - 2016
- [c16]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: 892-901 - [c15]Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen:
Communication Quantization for Data-Parallel Training of Deep Neural Networks. MLHPC@SC 2016: 1-8 - [c14]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:
Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications. SC 2016: 130-141 - 2015
- [j1]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): 15-28 (2015) - [c13]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: 359-364 - [c12]Brian Van Essen, Hyojin Kim, Roger A. Pearce, Kofi Boakye, Barry Chen:
LBANN: livermore big artificial neural network HPC toolkit. MLHPC@SC 2015: 5:1-5:6 - [i1]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 abs/1502.03409 (2015) - 2014
- [c11]Ming Jiang, Brian Van Essen, Cyrus Harrison, Maya B. Gokhale:
Multi-threaded streamline tracing for data-intensive architectures. LDAV 2014: 11-18 - 2012
- [c10]Brian Van Essen, Chris Macaraeg, Maya B. Gokhale, Ryan Prenger:
Accelerating a Random Forest Classifier: Multi-Core, GP-GPU, or FPGA? FCCM 2012: 232-239 - [c9]Dries Kimpe, Kathryn M. 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: 4:1-4:6 - [c8]Brian Van Essen, Roger A. Pearce, Sasha Ames, Maya B. Gokhale:
On the Role of NVRAM in Data-intensive Architectures: An Evaluation. IPDPS 2012: 703-714 - [c7]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: 731-735 - 2011
- [c6]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: 107-110 - 2010
- [c5]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: 380-387
2000 – 2009
- 2009
- [c4]Stephen Friedman, Allan Carroll, Brian Van Essen, Benjamin Ylvisaker, Carl Ebeling, Scott Hauck:
SPR: an architecture-adaptive CGRA mapping tool. FPGA 2009: 191-200 - [c3]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: 268-275 - 2006
- [c2]Benjamin Ylvisaker, Brian Van Essen, Carl Ebeling:
A Type Architecture for Hybrid Micro-Parallel Computers. FCCM 2006: 99-110 - [c1]Benjamin Ylvisaker, Brian Van Essen, Carl Ebeling:
A type architecture for hybrid micro-parallel computers. FPGA 2006: 227
Coauthor Index
Sam Ade Jacobs
aka: Sam Adé Jacobs
aka: Sam Adé Jacobs
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