Adapting the serial Alpgen event generator to simulate LHC collisions on millions of parallel threads. - John T. Childers; Thomas D. Uram; Thomas J. LeCompte; Michael E. Papka; D. P. Benjamin (CoRR, 2015)

Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads. - John T. Childers; Thomas D. Uram; Thomas J. LeCompte; Michael E. Papka; D. P. Benjamin (Comput. Phys. Commun., 2017)

Balsam: Near Real-Time Experimental Data Analysis on Supercomputers. - Michael A. Salim; Thomas D. Uram; J. Taylor Childers; Venkatram Vishwanath; Michael E. Papka (XLOOP@SC, 2019)

Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows. - Michael A. Salim; Thomas D. Uram; J. Taylor Childers; Prasanna Balaprakash; Venkatram Vishwanath; Michael E. Papka (CoRR, 2019)

Deep Reinforcement Agent for Scheduling in HPC. - Yuping Fan; Zhiling Lan; J. Taylor Childers; Paul Rich; William E. Allcock; Michael E. Papka (IPDPS, 2021)

Toward Real-time Analysis of Experimental Science Workloads on Geographically Distributed Supercomputers. - Michael Salim; Thomas D. Uram; J. Taylor Childers; Venkat Vishwanath; Michael E. Papka (CoRR, 2021)

Developments in Performance and Portability for MadGraph5_aMC@NLO. - Andrea Valassi; J. Taylor Childers; Laurence Field; Stefan Hageböck; Walter Hopkins; Olivier Mattelaer; Nathan S. Nichols; Stefan Roiser; David Smith (CoRR, 2022)

DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing. - Yuping Fan; Boyang Li; Dustin Favorite; Naunidh Singh; John Taylor Childers; Paul Rich; William E. Allcock; Michael E. Papka; Zhiling Lan (IEEE Trans. Parallel Distributed Syst., 2022)

Improving Performance Portability of the Procedurally Generated High Energy Physics Event Generator MadGraph Using SYCL. - Nathan S. Nichols; J. Taylor Childers; Tyler James Burch; Laurence Field (IWOCL, 2024)

SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver. - Daniel Lersch; Malachi Schram; Zhenyu Dai; Kishansingh Rajput; Xingfu Wu; N. Sato; J. Taylor Childers (CoRR, 2024)

SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver. - Daniel Lersch; Malachi Schram; Zhenyu Dai; Kishansingh Rajput; Nobuo Sato; Xingfu Wu; J. Taylor Childers; Steven Goldenberg (Mach. Learn. Sci. Technol., 2025)