HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languages
A. Chaturvedi*, D. Nichols*, S. Singh, A. Bhatele. HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languages. ISC 2025.
Publications
A Probabilistic Approach To Selecting Build Configurations in Package Managers
D. Nichols, H. Menon, T. Gamblin, A. Bhatele. A Probabilistic Approach To Selecting Build Configurations in Package Managers. SC 2024.
Relative Performance Prediction Using Few-Shot Learning
A. Dey, A. Dhakal, T. Islam, JS. Yeom, T. Patki, D. Nichols, A. Movsesyan, A. Bhatele. Relative Performance Prediction Using Few-Shot Learning. COMPSAC 2024.
Can Large Language Models Write Parallel Code?
D. Nichols, J. H. Davis, Z. Xie, A. Rajaram, A. Bhatele. Can Large Language Models Write Parallel Code?. HPDC 2024.
HPC-Coder: Modeling Parallel Programs using Large Language Models
D. Nichols, A. Marathe, H. Menon, T. Gamblin, A. Bhatele. ISC 2024.
Performance-Aligned LLMs for Generating Fast Code
D. Nichols, P. Polasam, H. Menon, A. Marathe, T. Gamblin, A. Bhatele. Performance-Aligned LLMs for Generating Fast Code. arXiv. cs.DC. 2404.18864. 2024.
Learning to Predict and Improve Build Successes in Package Ecosystems
H. Menon∗, D. Nichols∗, A. Bhatele, T. Gamblin. Learning to Predict and Improve Build Successes in Package Ecosystems. MSR 2024. * Authors contributed equally.
Predicting Cross-Architecture Performance of Parallel Programs
D. Nichols, A. Movsesyan, J-S Yeom, D. Milroy, T. Patki, A. Sarkar, A. Bhatele. Predicting Cross-Architecture Performance of Parallel Programs. IPDPS 2024.
Porting a Computational Fluid Dynamics Code with AMR to Large-scale GPU Platforms
J. H. Davis, J. Shafner, D. Nichols, N. Grube, P. Martin, A. Bhatele. IPDPS. 2023.
A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks
D. Nichols, S. Singh, S.-H. Lin, A. Bhatele. arXiv [cs.LG]. 2022.
Resource Utilization Aware Job Scheduling to Mitigate Performance Variability
D. Nichols, A. Marathe, K. Shoga, T. Gamblin, A. Bhatele. IPDPS. 2022.
Integrating Deep Learning in Domain Sciences at Exascale
R. Archibald, E. Chow, E. D’Azevedo, J. Dongarra, M. Eisenbach, R. Febbo, F. Lopez, D. Nichols, S. Tomov, K. Wong, and J. Yin. SMC. 2020.
openDIEL: A Parallel Workflow Engine And Data Analytics Framework
F. Betancourt, K. Wong, E. Asemota, Q. Marshall, D. Nichols, and S. Tomov. PEARC. 2019.
MagmaDNN: Accelerated Deep Learning Using Magma
D. Nichols, K. Wong, S. Tomov, L. Ng, S. Chen, and A. Gessinger. PEARC. 2019.
MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing
D. Nichols, N.-S. Tomov, F. Betancourt, S. Tomov, K. Wong, en J. Dongarra. ISC. 2019.