Steven Farrell

According to our database, Steven Farrell authored at least 15 papers between 2018 and 2024.

Collaborative distances:

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

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, Rémy Lyscar, Alexandre Michon, Benjamin Nachman, Peter Nugent, Mathis Reymond, David Rousseau, Benjamin Sluijter, Benjamin Thorne, Ihsan Ullah, Yulei Zhang

CoRR, 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, Nicholas J. Wright, Wahid Bhimji

CoRR, 2024

A Workflow Roofline Model for End-to-End Workflow Performance Analysis.

Nan Ding, Brian Austin, Yang Liu, Neil Mehta, Steven Farrell, Johannes P. Blaschke, Leonid Oliker, Hai Ah Nam, Nicholas J. Wright, Samuel Williams

Proceedings of the International Conference for High Performance Computing, 2024

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, Heather M. Gray, Isabelle Guyon, Mikhail Hushchyn, Vincenzo Innocente, Moritz Kiehn, Marcel Kunze, Edward Moyse, David Rousseau, Andreas Salzburger, Andrey Ustyuzhanin, Jean-Roch Vlimant

Comput. Softw. Big Sci., December, 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., June, 2023

Hierarchical Graph Neural Networks for Particle Track Reconstruction.

Ryan Liu, Paolo Calafiura, Steven Farrell, Xiangyang Ju, Daniel Thomas Murnane, Tuan Minh Pham

CoRR, 2023

2022

Benchmarking GPU and TPU Performance with Graph Neural Networks.

Xiangyang Ju, Yunsong Wang, Daniel Murnane, Nicholas Choma, Steven Farrell, Paolo Calafiura

CoRR, 2022

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, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin

CoRR, 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, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Markus Atkinson, Mark S. Neubauer, Gage DeZoort, Savannah Thais, Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu, Alexandra Ballow, Alina Lazar

CoRR, 2021

Hierarchical Roofline Performance Analysis for Deep Learning Applications.

Charlene Yang, Yunsong Wang, Thorsten Kurth, Steven Farrell, Samuel Williams

Proceedings of the Intelligent Computing, 2021

Architectural Requirements for Deep Learning Workloads in HPC Environments.

Khaled Z. Ibrahim, Tan Nguyen, Hai Ah Nam, Wahid Bhimji, Steven Farrell, Leonid Oliker, Michael Rowan, Nicholas J. Wright, Samuel Williams

Proceedings of the 2021 International Workshop on Performance Modeling, 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, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin

Proceedings of the IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, 2021

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, Jean-Roch Vlimant, Maria Spiropulu, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy L. Usher

CoRR, 2020

Time-Based Roofline for Deep Learning Performance Analysis.

Yunsong Wang, Charlene Yang, Steven Farrell, Yan Zhang, Thorsten Kurth, Samuel Williams

Proceedings of the Fourth IEEE/ACM Workshop on Deep Learning on Supercomputers, 2020

2018

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, Ed Moyse, David Rousseau, Yetkin Yilmaz, Vladimir Vava Gligorov, Mikhail Hushchyn, Andrey Ustyuzhanin

Proceedings of the 14th IEEE International Conference on e-Science, 2018