Background

J. Austin Ellis is an HPC Research Scientist in the Analytics and AI Methods at Scale group within the Oak Ridge Leadership Computing Facility (OLCF). His research is primarily focused on machine learning modeling for science applications, algorithmic scalability, and advanced facility analytics. He has a PhD in Applied Mathematics from North Carolina State University and was a postdoc in the Scalable Algorithms group at Sandia National Laboratories. From 2016 to 2018, he was a PhD intern at ORNL in the HPC Methods for Nuclear Applications group working on the leadership Shift Monte Carlo radiation transport code.

Education

2018
North Carolina State University
Applied Mathematics
Doctor of Philosophy (Ph.D.)

Awards

2021 — Best Paper, "Revealing power, energy and thermal dynamics of a 200PF pre-exascale supercomputer" at SC21

2019 — Innovation Award, "Scalable Inference for Sparse Deep Neural Networks using Kokkos Kernels" at HPEC 2019 Graph Challenge (Sparse ML)

Publications

2021
Frank J. Alexander, et al. Co-design Center for Exascale Machine Learning Technologies (ExaLearn). The International Journal of High Performance Computing Applications, 35(6):598-616 (2021). doi:https://doi.org/10.1177/10943420211029302
2021
J. Austin Ellis, Lenz Fiedler, Gabe A. Popoola, Normand A. Modine, J. Adam Stephens, Aidan P. Thompson, Attila Cangi, and Siva Rajamanickam. Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks. Phys. Rev. B 104, 035120 (2021). doi:https://doi.org/10.1103/PhysRevB.104.035120
2021
Woong Shin, Vladyslav Oles, Ahmad Maroof Karimi, J. Austin Ellis, and Feiyi Wang. 2021. Revealing power, energy and thermal dynamics of a 200PF pre-exascale supercomputer. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '21). Association for Computing Machinery, New York, NY, USA, Article 12, 1–14. doi:https://doi.org/10.1145/3458817.3476188
2020
Gordon E. Moon, J. Austin Ellis, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, and P. Sadayappan. 2020. ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '20). Association for Computing Machinery, New York, NY, USA, 1758–1767. doi:https://doi.org/10.1145/3394486.3403227
2019
J. Austin Ellis and Siva Rajamanickam, Scalable Inference for Sparse Deep Neural Networks using Kokkos Kernels. 2019 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 2019, pp. 1-7, doi:https://doi.org/10.1109/HPEC.2019.8916378