HPC-Coder: Modeling Parallel Programs using Large Language Models D Nichols, A Marathe, H Menon, T Gamblin, A Bhatele ISC High Performance, 2024 | 30* | 2024 |
Can large language models write parallel code? D Nichols, JH Davis, Z Xie, A Rajaram, A Bhatele Proceedings of the 33rd International Symposium on High-Performance Parallel …, 2024 | 19 | 2024 |
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 | 12 | 2019 |
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 | 11 | 2022 |
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 | 11 | 2020 |
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 | 10* | 2021 |
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 | 10 | 2019 |
Performance-Aligned LLMs for Generating Fast Code D Nichols, P Polasam, H Menon, A Marathe, T Gamblin, A Bhatele arXiv preprint arXiv:2404.18864, 2024 | 7 | 2024 |
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 |
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 | 3 | 2023 |
Learning to Predict and Improve Build Successes in Package Ecosystems H Menon, D Nichols, A Bhatele, T Gamblin 2024 IEEE/ACM 21st International Conference on Mining Software Repositories …, 2024 | 2 | 2024 |
A Probabilistic Approach To Selecting Build Configurations in Package Managers D Nichols, H Menon, T Gamblin, A Bhatele SC24: International Conference for High Performance Computing, Networking …, 2024 | 1 | 2024 |
HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languages A Chaturvedi, D Nichols, S Singh, A Bhatele arXiv preprint arXiv:2412.15178, 2024 | | 2024 |
Relative Performance Prediction Using Few-Shot Learning A Dey, A Dhakal, TZ Islam, JS Yeom, T Patki, D Nichols, A Movsesyan, ... 2024 IEEE 48th Annual Computers, Software, and Applications Conference …, 2024 | | 2024 |
Predicting Cross-Architecture Performance of Parallel Programs D Nichols, A Movsesyan, JS Yeom, A Sarkar, D Milroy, T Patki, A Bhatele 2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2024 | | 2024 |
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 |
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 Across Architectures 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 |