Communication Quantization for Data-Parallel Training of Deep Neural Networks. - Nikoli Dryden; Tim Moon; Sam Ade Jacobs; Brian Van Essen (MLHPC@SC, 2016)

Accelerating eigenvector and pseudospectra computation using blocked multi-shift triangular solves. - Tim Moon; Jack Poulson (CoRR, 2016)

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; et al. (CLUSTER, 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 (IPDPS, 2019)

Channel and filter parallelism for large-scale CNN training. - Nikoli Dryden; Naoya Maruyama; Tim Moon; Tom Benson; Marc Snir; Brian Van Essen (SC, 2019)

SUPER: SUb-Graph Parallelism for TransformERs. - Arpan Jain; Tim Moon; Tom Benson; Hari Subramoni; Sam Adé Jacobs; Dhabaleswar K. Panda 001; Brian Van Essen (IPDPS, 2021)

Learning Interpretable Models Through Multi-Objective Neural Architecture Search. - Zachariah Carmichael; Tim Moon; Sam Ade Jacobs (CoRR, 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)

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 (CCGRID, 2022)