ACM SRC poster: from days to seconds: scalable parallel algorithm for motion planning. - Sam Ade Jacobs; Nancy M. Amato (SC Companion, 2011)

A scalable method for parallelizing sampling-based motion planning algorithms. - Sam Ade Jacobs; Kasra Manavi; Juan Burgos; Jory Denny; Shawna L. Thomas; Nancy M. Amato (ICRA, 2012)

Local randomization in neighbor selection improves PRM roadmap quality. - Troy McMahon; Sam Ade Jacobs; Bryan Boyd; Lydia Tapia; Nancy M. Amato (IROS, 2012)

A scalable distributed RRT for motion planning. - Sam Ade Jacobs; Nicholas Stradford; Cesar Rodriguez; Shawna L. Thomas; Nancy M. Amato (ICRA, 2013)

Adaptive neighbor connection for PRMs: A natural fit for heterogeneous environments and parallelism. - Chinwe Ekenna; Sam Ade Jacobs; Shawna L. Thomas; Nancy M. Amato (IROS, 2013)

Blind RRT: A probabilistically complete distributed RRT. - Cesar Rodriguez; Jory Denny; Sam Ade Jacobs; Shawna L. Thomas; Nancy M. Amato (IROS, 2013)

Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms. - Adam Fidel; Sam Ade Jacobs; Shishir Sharma; Nancy M. Amato; Lawrence Rauchwerger (IPDPS, 2014)

The anatomy of a distributed motion planning roadmap. - Sam Ade Jacobs; Nancy M. Amato (IROS, 2014)

Industrial Analytics Pipelines. - K. Eric Harper; Jiang Zheng 001; Sam Ade Jacobs; Aldo Dagnino; Anton Jansen; Thomas Goldschmidt; Adamantios Marinakis (BigDataService, 2015)

Large-Scale Industrial Alarm Reduction and Critical Events Mining Using Graph Analytics on Spark. - Sam Ade Jacobs; Aldo Dagnino (BigDataService, 2016)

Graph-based clustering for detecting frequent patterns in event log data. - Erika Sy; Sam Ade Jacobs; Aldo Dagnino; Yu Ding 002 (CASE, 2016)

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

Towards Scalable Parallel Training of Deep Neural Networks. - Sam Ade Jacobs; Nikoli Dryden; Roger A. Pearce; Brian Van Essen (MLHPC@SC, 2017)

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)

Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency. - Ya Ju Fan; Jonathan E. Allen; Sam Ade Jacobs; Brian C. Van Essen (CoRR, 2019)

Merlin: Enabling Machine Learning-Ready HPC Ensembles. - J. Luc Peterson; Rushil Anirudh; Kevin Athey; Benjamin Bay; Peer-Timo Bremer; Vic Castillo; Francesco Di Natale; David Fox; Jim A. Gaffney; David Hysom; Sam Ade Jacobs; Bhavya Kailkhura; et al. (CoRR, 2019)

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. - Shusen Liu 001; Jim Gaffney; J. Luc Peterson; Peter B. Robinson; Harsh Bhatia; Valerio Pascucci; Brian K. Spears; Peer-Timo Bremer; Di Wang; Dan Maljovec; Rushil Anirudh; Jayaraman J. Thiagarajan; et al. (IEEE Trans. Vis. Comput. Graph., 2020)

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)

Scalable Composition and Analysis Techniques for Massive Scientific Workflows. - Dong H. Ahn; Xiaohua Zhang; Jeffrey Mast; Stephen Herbein; Francesco Di Natale; Dan Kirshner 001; Sam Ade Jacobs; Ian Karlin; Daniel J. Milroy; Bronis R. de Supinski; Brian Van Essen; Jonathan E. Allen; et al. (e-Science, 2022)

Enabling machine learning-ready HPC ensembles with Merlin. - J. Luc Peterson; Benjamin Bay; Joe Koning; Peter B. Robinson; Jessica Semler; Jeremy White; Rushil Anirudh; Kevin Athey; Peer-Timo Bremer; Francesco Di Natale; David Fox; Jim A. Gaffney; et al. (Future Gener. Comput. Syst., 2022)

ZeRO++: Extremely Efficient Collective Communication for Giant Model Training. - Guanhua Wang; Heyang Qin; Sam Ade Jacobs; Connor Holmes; Samyam Rajbhandari; Olatunji Ruwase; Feng Yan 001; Lei Yang 001; Yuxiong He (CoRR, 2023)

DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models. - Sam Ade Jacobs; Masahiro Tanaka; Chengming Zhang 006; Minjia Zhang; Shuaiwen Leon Song; Samyam Rajbhandari; Yuxiong He (CoRR, 2023)

ZeRO++: Extremely Efficient Collective Communication for Large Model Training. - Guanhua Wang; Heyang Qin; Sam Ade Jacobs; Xiaoxia Wu; Connor Holmes; Zhewei Yao; Samyam Rajbhandari; Olatunji Ruwase; Feng Yan 001; Lei Yang 001; Yuxiong He (ICLR, 2024)

System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models. - Sam Ade Jacobs; Masahiro Tanaka; Chengming Zhang 006; Minjia Zhang; Reza Yazdani Aminabadi; Shuaiwen Leon Song; Samyam Rajbhandari; Yuxiong He (IPDPS (Workshops), 2024)

System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models. - Sam Ade Jacobs; Masahiro Tanaka; Chengming Zhang 006; Minjia Zhang; Reza Yazdani Aminadabi; Shuaiwen Leon Song; Samyam Rajbhandari; Yuxiong He (PODC, 2024)

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone. - Marah I Abdin; Sam Ade Jacobs; Ammar Ahmad Awan; Jyoti Aneja; Ahmed Awadallah 001; Hany Awadalla; Nguyen Bach; Amit Bahree; Arash Bakhtiari; Harkirat S. Behl; Alon Benhaim; Misha Bilenko; et al. (CoRR, 2024)

Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training. - Xinyu Lian; Sam Ade Jacobs; Lev Kurilenko; Masahiro Tanaka; Stas Bekman; Olatunji Ruwase; Minjia Zhang (CoRR, 2024)

Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer. - Jinghan Yao; Sam Ade Jacobs; Masahiro Tanaka; Olatunji Ruwase; Aamir Shafi; Hari Subramoni; Dhabaleswar K. Panda 001 (CoRR, 2024)

Universal Checkpointing: A Flexible and Efficient Distributed Checkpointing System for Large-Scale DNN Training with Reconfigurable Parallelism. - Xinyu Lian; Sam Ade Jacobs; Lev Kurilenko; Masahiro Tanaka; Stas Bekman; Olatunji Ruwase; Minjia Zhang (USENIX ATC, 2025)