TrackML: A High Energy Physics Particle Tracking Challenge. - Paolo Calafiura; Steven Farrell; Heather M. Gray; Jean-Roch Vlimant; Vincenzo Innocente; Andreas Salzburger; Sabrina Amrouche; Tobias Golling; Moritz Kiehn; Victor Estrade; Cécile Germain; Isabelle Guyon; et al. (eScience, 2018)

Time-Based Roofline for Deep Learning Performance Analysis. - Yunsong Wang; Charlene Yang; Steven Farrell; Yan Zhang; Thorsten Kurth; Samuel Williams 001 (DLS@SC, 2020)

Track Seeding and Labelling with Embedded-space Graph Neural Networks. - Nicholas Choma; Daniel Murnane; Xiangyang Ju; Paolo Calafiura; Sean Conlon; Steven Farrell; Prabhat; Giuseppe Cerati; Lindsey Gray; Thomas Klijnsma; Jim Kowalkowski; Panagiotis Spentzouris; et al. (CoRR, 2020)

MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (MLHPC@SC, 2021)

Architectural Requirements for Deep Learning Workloads in HPC Environments. - Khaled Z. Ibrahim; Tan Nguyen 001; Hai Ah Nam; Wahid Bhimji; Steven Farrell; Leonid Oliker; Michael Rowan; Nicholas J. Wright; Samuel Williams 001 (PMBS, 2021)

Hierarchical Roofline Performance Analysis for Deep Learning Applications. - Charlene Yang; Yunsong Wang; Thorsten Kurth; Steven Farrell; Samuel Williams 001 (SAI (2), 2021)

Physics and Computing Performance of the Exa.TrkX TrackML Pipeline. - Xiangyang Ju; Daniel Murnane; Paolo Calafiura; Nicholas Choma; Sean Conlon; Steven Farrell; Yaoyuan Xu; Maria Spiropulu; Jean-Roch Vlimant; Adam Aurisano; Jeremy Hewes; Giuseppe Cerati; et al. (CoRR, 2021)

MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (CoRR, 2021)

Benchmarking GPU and TPU Performance with Graph Neural Networks. - Xiangyang Ju; Yunsong Wang; Daniel Murnane; Nicholas Choma; Steven Farrell; Paolo Calafiura (CoRR, 2022)

Hierarchical Graph Neural Networks for Particle Track Reconstruction. - Ryan Liu; Paolo Calafiura; Steven Farrell; Xiangyang Ju; Daniel Thomas Murnane; Tuan Minh Pham (CoRR, 2023)

Rapid Prediction of a Liquid Structure from a Single Molecular Configuration Using Deep Learning. - Chunhui Li; Benjamin Gilbert; Steven Farrell; Piotr Zarzycki (J. Chem. Inf. Model., 2023)

The Tracking Machine Learning Challenge: Throughput Phase. - Sabrina Amrouche; Laurent Basara; Paolo Calafiura; Dmitry Emeliyanov; Victor Estrade; Steven Farrell; Cécile Germain; Vladimir Vava Gligorov; Tobias Golling; Sergey Gorbunov 002; Heather M. Gray; Isabelle Guyon; et al. (Comput. Softw. Big Sci., 2023)

A Workflow Roofline Model for End-to-End Workflow Performance Analysis. - Nan Ding 006; Brian Austin; Yang Liu 179; Neil Mehta; Steven Farrell; Johannes P. Blaschke; Leonid Oliker; Hai Ah Nam; Nicholas J. Wright; Samuel Williams 001 (SC, 2024)

Comprehensive Performance Modeling and System Design Insights for Foundation Models. - Shashank Subramanian; Ermal Rrapaj; Peter Harrington; Smeet Chheda; Steven Farrell; Brian Austin; Samuel Williams 001; Nicholas J. Wright; Wahid Bhimji (SC Workshops, 2024)

FAIR Universe HiggsML Uncertainty Challenge Competition. - Wahid Bhimji; Paolo Calafiura; Ragansu Chakkappai; Yuan-Tang Chou; Sascha Diefenbacher; Jordan Dudley; Steven Farrell; Aishik Ghosh; Isabelle Guyon; Chris Harris; Shih-Chieh Hsu; Elham E Khoda; et al. (CoRR, 2024)

Understanding the Landscape of Ampere GPU Memory Errors. - Zhu Zhu; Yu Sun; Dhatri Parakal; Bo Fang 002; Steven Farrell; Gregory H. Bauer; Brett M. Bode; Ian T. Foster; Michael E. Papka; William Gropp; Zhao Zhang 007; Lishan Yang (CoRR, 2025)