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)

Towards Large-scale Drive-by Sensing with Multi-purpose City Scanner Nodes. - Simone Mora; Amin Anjomshoaa; Tom Benson; Fábio Duarte; Carlo Ratti (WF-IoT, 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)

Detecting languages in streetscapes using deep convolutional neural networks. - You Xuan Thung; Tom Benson; Nikita Klimenko (IEEE Big Data, 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 (CCGRID, 2022)