Tim Moon

According to our database, Tim Moon authored at least 9 papers between 2016 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2022

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

Shehtab Zaman, Tim Moon, Tom Benson, Sam Adé Jacobs, Kenneth Chiu, Brian Van Essen

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

2021

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

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

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

Learning Interpretable Models Through Multi-Objective Neural Architecture Search.

Zachariah Carmichael, Tim Moon, Sam Ade Jacobs

CoRR, 2021

SUPER: SUb-Graph Parallelism for TransformERs.

Arpan Jain, Tim Moon, Tom Benson, Hari Subramoni, Sam Adé Jacobs, Dhabaleswar K. Panda, Brian Van Essen

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

2019

Channel and filter parallelism for large-scale CNN training.

Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen

Proceedings of the International Conference for High Performance Computing, 2019

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

Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen

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

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets.

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

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

2016

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

Tim Moon, Jack Poulson

CoRR, 2016

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

Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen

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