| Modeling Parallel Programs using Large Language Models D Nichols, A Marathe, H Menon, T Gamblin, A Bhatele arXiv preprint arXiv:2306.17281, 2023 | 10 | 2023 |
| Integrating deep learning in domain sciences at exascale R Archibald, E Chow, E D’Azevedo, J Dongarra, M Eisenbach, R Febbo, ... Driving Scientific and Engineering Discoveries Through the Convergence of …, 2020 | 10 | 2020 |
| MagmaDNN: Accelerated Deep Learning Using MAGMA D Nichols, K Wong, S Tomov, L Ng, S Chen, A Gessinger Proceedings of the Practice and Experience in Advanced Research Computing on …, 2019 | 9 | 2019 |
| MagmaDNN: towards high-performance data analytics and machine learning for data-driven scientific computing D Nichols, NS Tomov, F Betancourt, S Tomov, K Wong, J Dongarra International Conference on High Performance Computing, 490-503, 2019 | 9 | 2019 |
| A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks D Nichols, S Singh, SH Lin, A Bhatele arXiv e-prints, arXiv: 2111.04949, 2021 | 6* | 2021 |
| Resource Utilization Aware Job Scheduling to Mitigate Performance Variability D Nichols, A Marathe, K Shoga, T Gamblin, A Bhatele 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2022 | 4 | 2022 |
| openDIEL: A Parallel Workflow Engine and Data Analytics Framework F Betancourt, K Wong, E Asemota, Q Marshall, D Nichols, S Tomov Proceedings of the Practice and Experience in Advanced Research Computing on …, 2019 | 4 | 2019 |
| Can Large Language Models Write Parallel Code? D Nichols, JH Davis, Z Xie, A Rajaram, A Bhatele arXiv preprint arXiv:2401.12554, 2024 | 2 | 2024 |
| Porting a Computational Fluid Dynamics Code with AMR to Large-scale GPU Platforms JH Davis, J Shafner, D Nichols, N Grube, P Martin, A Bhatele 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2023 | 1 | 2023 |
| Automated Programmatic Performance Analysis of Parallel Programs O Cankur, A Tomar, D Nichols, C Scully-Allison, KE Isaacs, A Bhatele arXiv preprint arXiv:2401.13150, 2024 | | 2024 |
| A Probabilistic Approach To Selecting Build Configurations in Package Managers D Nichols, H Menon, A Bhatele, T Gamblin Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023 | | 2023 |
| Learning to Predict and Improve Build Successes in Package Ecosystems H Menon, D Nichols, A Bhatele, T Gamblin Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023 | | 2023 |
| Relative Performance Prediction using Semi-Supervised Learning A Dey, T Islam, J Yeom, T Patki, D Nichols, A Movsesyan, A Bhatele Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023 | | 2023 |
| Learning to Predict Performance of Parallel Applications AcrossArchitectures D Nichols, A Movsesyan, J Yeom, D Milroy, T Patki, A Sarkar, A Bhatele Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023 | | 2023 |
| Crystallographic Lattice Classification with Deep Learning and MagmaDNN S Keh, D Nichols, KF Chan | | 2019 |
| MagmaDNN and Ising Physics Simulations with Graph Convolutional Network KF Chan, D Nichols, S Keh | | 2019 |
| MagmaDNN: Towards High-Performance Deep Learning Using Magma D Nichols, S Keh, KF Chan | | 2019 |
| HPC-Coder: Modeling Parallel Programs using Large Language Models D Nichols, A Marathe, H Menon, T Gamblin, A Bhatele | | |
| Predicting Cross-Architecture Performance of Parallel Programs D Nichols, A Movsesyan, JS Yeom, A Sarkar, D Milroy, T Patki, A Bhatele | | |
| Large-scale GPU Computational Fluid Dynamics with AMR J Davis, J Shafner, D Nichols, N Grube, P Martín, A Bhatele | | |
Abhinav BhateleAssociate Professor of Computer Science, University of Maryland, College ParkVerified email at cs.umd.edu
