Tim Moon

Communication Quantization for Data-Parallel Training of Deep Neural Networks

Sam Ade Jacobs, Brian Van Essen, S. A. Jacobs, 2016, 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC).

Panta Rhei - Towards an Objective Understanding of Flowing Time

Tim Moon, 2016 .

Aluminum: An Asynchronous, GPU-Aware Communication Library Optimized for Large-Scale Training of Deep Neural Networks on HPC Systems

Marc Snir, Tom Benson, Brian Van Essen, 2018, 2018 IEEE/ACM Machine Learning in HPC Environments (MLHPC).

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

Marc Snir, Tom Benson, Brian Van Essen, 2019, 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

Channel and filter parallelism for large-scale CNN training

Marc Snir, Tom Benson, Brian Van Essen, 2019, SC.

Livermore Big Artificial Neural Network Toolkit

Sam Ade Jacobs, Hyojin Kim, Brian Van Essen, 2016 .

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

Sam Ade Jacobs, Peer-Timo Bremer, Tom Benson, 2019, 2019 IEEE International Conference on Cluster Computing (CLUSTER).

Accelerating eigenvector and pseudospectra computation using blocked multi-shift triangular solves pdf

Jack Poulson, Tim Moon, J. Poulson, 2016, ArXiv.

Learning Interpretable Models Through Multi-Objective Neural Architecture Search pdf

Sam Ade Jacobs, Tim Moon, Zachariah Carmichael, 2021, ArXiv.

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

Sam Ade Jacobs, Felice C Lightstone, S. A. Jacobs, 2021, Int. J. High Perform. Comput. Appl..