Completed PhD at UMD
Published:
Wrapped up my PhD at the University of Maryland.
Posts by Collection
news
Attending ISC in Hamburg
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I’ll be at ISC in Hamburg. Come say hi!
Began as Sidney Fernbach Fellow at LLNL Permalink
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Beginning as a postdoc at LLNL
Married!
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Not career related, but got married!
Attending ICPP in San Diego
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I’ll be at ICPP in San Diego. Come say hi!
Paper on optimizing GPU kernels with reasoning models uploaded to arXiv Permalink
Published:
New preprint on using reasoning models to optimize GPU kernels.
Paper on Spack package generation with agents uploaded to arXiv Permalink
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New preprint on automating Spack package generation with agents.
Attending Supercomputing in St. Louis
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Come say hi at SC in St. Louis!
notes
Graphics Basics
Notes on computer graphics basics
Probability and Random Variables
Notes on computer probability and random variables.
Reproducing Kernel Hilbert Spaces
Notes on reproducing kernel hilbert spaces and applications in machine learning
Statistical Pattern Recognition
Notes on statistical pattern recognition and machine learning foundations.
publications
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.
MagmaDNN: Accelerated Deep Learning Using Magma
D. Nichols, K. Wong, S. Tomov, L. Ng, S. Chen, and A. Gessinger. PEARC. 2019.
openDIEL: A Parallel Workflow Engine And Data Analytics Framework
F. Betancourt, K. Wong, E. Asemota, Q. Marshall, D. Nichols, and S. Tomov. PEARC. 2019.
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.
Resource Utilization Aware Job Scheduling to Mitigate Performance Variability
D. Nichols, A. Marathe, K. Shoga, T. Gamblin, A. Bhatele. IPDPS. 2022.
A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks
D. Nichols, S. Singh, S.-H. Lin, A. Bhatele. arXiv [cs.LG]. 2022.
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.
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.
Automated Programmatic Performance Analysis of Parallel Programs
O. Cankur, A. Tomar, D. Nichols, C. Scully-Allison, K. E. Isaacs, A. Bhatele. Automated Programmatic Performance Analysis of Parallel Programs. arXiv. 2401.13150. 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.
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.
HPC-Coder: Modeling Parallel Programs using Large Language Models
D. Nichols, A. Marathe, H. Menon, T. Gamblin, A. Bhatele. ISC 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.
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.
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.
The Shortcomings of Code LLMs in Modeling Code Properties
S. Pyda, D. Nichols, A. Bhatele. The Shortcomings of Code LLMs in Modeling Code Properties. LLM4Code 2025.
xAMM: “Attention” to Details Improve Cross-Platform Prediction Accuracy
A. R. Dhakal, T. Z. Islam, A. Dey, T. Patki, D. Nichols, A. Bhatele, J.-S. Yeom. xAMM: "Attention" to Details Improve Cross-Platform Prediction Accuracy. CCGrid 2025.
Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
K. Teranishi, H. Menon, W. F Godoy, P. Balaprakash, D. Bau, T. Ben-Nun, A. Bhatele, F. Franchetti, M. Franusich, T. Gamblin, G. Georgakoudis, T. Goldstein, A. Guha, S. Hahn, C. Iancu, Z. Jin, T. Jones, T. Meng Low, H. Mankad, N. Rao Miniskar, M. Alaul Haque Monil, D. Nichols, K. Parasyris, S. Pophale, P. Valero-Lara, J. S. Vetter, S. Williams, A. Young. Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions. LLM4HPC 2025.
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.
Modeling Code: Is Text All You Need?
D. Nichols, K. Parasyris, H. Menon, B. R. Bartoldson, G. Georgakoudis, T. Ben-Nun, A. Bhatele. Modeling Code: Is Text All You Need? arXiv. 2507.11467. 2025.
ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks
J. H. Davis, D. Nichols, I. Khillan, A. Bhatele. ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks. ICPP 2025.
Integrating Performance Tools in Model Reasoning for GPU Kernel Optimization
D. Nichols, K. Parasyris, C. Jekel, A. Bhatele, H. Menon. Integrating Performance Tools in Model Reasoning for GPU Kernel Optimization. arXiv. 2510.17158. 2025.
LLMs as Packagers of HPC Software
C. Melone, D. Nichols, K. Parasyris, T. Gamblin, H. Menon. LLMs as Packagers of HPC Software. arXiv. 2511.05626. 2025.
software
Slurm Dashboard
A VSCode extension for interacting with HPC workload managers.
Performance Profile Viewer
A VSCode extension for viewing performance profiles
CSscholar
A website for viewing and analyzing CS publication data
MagmaDNN: High Performance Deep Learning
A High Performance Deep Learning Package
talks
teaching
Teaching Assistant
Course, University of Tennessee, Department of Computer Science, 2019
Teaching assistant for Computer Science 140: Data Structures and Algorithms I.