Brian Van Essen

Orcid: 0000-0002-2281-7500

Affiliations:

Lawrence Livermore National Laboratory, CA, USA

According to our database¹, Brian Van Essen authored at least 44 papers between 2006 and 2022.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2022

Enabling machine learning-ready HPC ensembles with Merlin.

[BibT_eX]

[DOI]

Future Gener. Comput. Syst., 2022

Scalable Composition and Analysis Techniques for Massive Scientific Workflows.

[BibT_eX]

[DOI]

Bronis R. de Supinski

Brian Van Essen

Jonathan E. Allen

Felice C. Lightstone

Proceedings of the 18th IEEE International Conference on e-Science, 2022

Parallelizing Graph Neural Networks via Matrix Compaction for Edge-Conditioned Networks.

[BibT_eX]

[DOI]

Proceedings of the 22nd IEEE International Symposium on Cluster, 2022

2021

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism.

[BibT_eX]

[DOI]

IEEE Trans. Parallel Distributed Syst., 2021

Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations.

[BibT_eX]

[DOI]

Nat. Mach. Intell., 2021

Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models.

[BibT_eX]

[DOI]

Int. J. High Perform. Comput. Appl., 2021

Co-design Center for Exascale Machine Learning Technologies (ExaLearn).

[BibT_eX]

[DOI]

Int. J. High Perform. Comput. Appl., 2021

Is Disaggregation possible for HPC Cognitive Simulation?

[BibT_eX]

[DOI]

Proceedings of the IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, 2021

SUPER: SUb-Graph Parallelism for TransformERs.

[BibT_eX]

[DOI]

Proceedings of the 35th IEEE International Parallel and Distributed Processing Symposium, 2021

Monitoring Large Scale Supercomputers: A Case Study with the Lassen Supercomputer.

[BibT_eX]

[DOI]

Bronis R. de Supinski

Nathan Besaw

Proceedings of the IEEE International Conference on Cluster Computing, 2021

2020

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications.

[BibT_eX]

[DOI]

Jayaraman J. Thiagarajan

IEEE Trans. Vis. Comput. Graph., 2020

2019

Merlin: Enabling Machine Learning-Ready HPC Ensembles.

[BibT_eX]

[DOI]

CoRR, 2019

Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency.

[BibT_eX]

[DOI]

CoRR, 2019

Preparation and optimization of a diverse workload for a large-scale heterogeneous system.

[BibT_eX]

[DOI]

Ian Karlin

Yoonho Park

Bronis R. de Supinski

Sara Kokkila Schumacher

Guillaume Thomas-Collignon

Proceedings of the International Conference for High Performance Computing, 2019

Channel and filter parallelism for large-scale CNN training.

[BibT_eX]

[DOI]

Proceedings of the International Conference for High Performance Computing, 2019

Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Parallel and Distributed Processing Symposium, 2019

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets.

[BibT_eX]

[DOI]

Jayaraman J. Thiagarajan

Shusen Liu

Peer-Timo Bremer

Proceedings of the 2019 IEEE International Conference on Cluster Computing, 2019

Argo.

[BibT_eX]

[DOI]

Proceedings of the Operating Systems for Supercomputers and High Performance Computing, 2019

2018

Modular Spiking Neural Circuits for Mapping Long Short-Term Memory on a Neurosynaptic Processor.

[BibT_eX]

[DOI]

IEEE J. Emerg. Sel. Topics Circuits Syst., 2018

The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.

[BibT_eX]

[DOI]

CoRR, 2018

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research.

[BibT_eX]

[DOI]

Cristina Garcia-Cardona

Brian Van Essen

Matthew Baughman

BMC Bioinform., 2018

Effective Quantization Approaches for Recurrent Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

2017

Towards Scalable Parallel Training of Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning on HPC Environments, 2017

Argo NodeOS: Toward Unified Resource Management for Exascale.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium, 2017

Quadratic Unconstrained Binary Optimization (QUBO) on neuromorphic computing system.

[BibT_eX]

[DOI]

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Convolutional sparse coding on neurosynaptic cognitive system.

[BibT_eX]

[DOI]

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

A spike-based long short-term memory on a neurosynaptic processor.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE/ACM International Conference on Computer-Aided Design, 2017

2016

Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications.

[BibT_eX]

[DOI]

Proceedings of the International Conference for High Performance Computing, 2016

Communication Quantization for Data-Parallel Training of Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 2nd Workshop on Machine Learning in HPC Environments, 2016

Towards a Distributed Large-Scale Dynamic Graph Data Store.

[BibT_eX]

[DOI]

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

2015

Large-Scale Deep Learning on the YFCC100M Dataset.

[BibT_eX]

[DOI]

CoRR, 2015

DI-MMAP - a scalable memory-map runtime for out-of-core data-intensive applications.

[BibT_eX]

[DOI]

Clust. Comput., 2015

LBANN: livermore big artificial neural network HPC toolkit.

[BibT_eX]

[DOI]

Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, 2015

A Container-Based Approach to OS Specialization for Exascale Computing.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Conference on Cloud Engineering, 2015

2014

Multi-threaded streamline tracing for data-intensive architectures.

[BibT_eX]

[DOI]

Proceedings of the 4th IEEE Symposium on Large Data Analysis and Visualization, 2014

2012

DI-MMAP: A High Performance Memory-Map Runtime for Data-Intensive Applications.

[BibT_eX]

[DOI]

Proceedings of the 2012 SC Companion: High Performance Computing, 2012

On the Role of NVRAM in Data-intensive Architectures: An Evaluation.

[BibT_eX]

[DOI]

Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium, 2012

Integrated in-system storage architecture for high performance computing.

[BibT_eX]

[DOI]

Bronis R. de Supinski

Proceedings of the 2nd International Workshop on Runtime and Operating Systems for Supercomputers, 2012

Accelerating a Random Forest Classifier: Multi-Core, GP-GPU, or FPGA?

[BibT_eX]

[DOI]

Proceedings of the 2012 IEEE 20th Annual International Symposium on Field-Programmable Custom Computing Machines, 2012

2011

Energy-efficient specialization of functional units in a coarse-grained reconfigurable array.

[BibT_eX]

[DOI]

Proceedings of the ACM/SIGDA 19th International Symposium on Field Programmable Gate Arrays, 2011

2010

Managing Short-Lived and Long-Lived Values in Coarse-Grained Reconfigurable Arrays.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Field Programmable Logic and Applications, 2010

2009

Static versus scheduled interconnect in Coarse-Grained Reconfigurable Arrays.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Field Programmable Logic and Applications, 2009

SPR: an architecture-adaptive CGRA mapping tool.

[BibT_eX]

[DOI]

Proceedings of the ACM/SIGDA 17th International Symposium on Field Programmable Gate Arrays, 2009

2006

A Type Architecture for Hybrid Micro-Parallel Computers.

[BibT_eX]

[DOI]

Benjamin Ylvisaker

Brian Van Essen

Carl Ebeling

Proceedings of the 14th IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2006), 2006

Brian Van Essen

Timeline

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Bibliography

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