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!
Paper on optimizing inference for agents with speculative tools uploaded to arXiv Permalink
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New preprint on optimizing inference for agents with speculative tools.
2026 Grand Challenges Award Permalink
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Received a 2026 Grand Challenges award at LLNL for ‘AI Assisted Scientific Code Optimization’.
Paper on performance aligned code LLMs accepted in TPDS Permalink
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Our paper “Performance-Aligned LLMs for Generating Fast HPC Code” was accepted into TPDS.
Paper on hierarchical GPU kernel optimization with evolutionary optimization posted to arXiv. Permalink
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Our paper “Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search” was posted to arXiv.
LongCoT accepted to ICML 2026 Permalink
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Our paper “LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning” was accepted to ICML 2026.
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.
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.
Optimizing Agentic Language Model Inference via Speculative Tool Calls
D. Nichols, P. Singhania, C. Jekel, A. Bhatele, H. Menon. arXiv. 2512.15834. 2025.
Performance-Aligned LLMs for Generating Fast HPC Code
D. Nichols et al., "Performance-Aligned LLMs for Generating Fast HPC Code" in IEEE Transactions on Parallel & Distributed Systems, vol. , no. 01, pp. 1-12, PrePrints 5555, doi: 10.1109/TPDS.2026.3675550.
Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
C. Wei, S. Singh, A. Sarkar, D. Nichols, T. Patel, A. K. Ranjan, S. Ghosh, A. Jannesari, N. R. Tallent, A. Bhatele. arXiv. 2604.02651. 2026.
Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search
D. Nichols, K. Parasyris, C. Melone, T. Ben-Nun, G. Georgakoudis, H. Menon. arXiv. 2604.11109. 2026.
LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
S. Ramesh Motwani*, D. Nichols*, C. London*, P. Li*, F. Pizzati*, A. Blake, H. Hammoud, T. McDonald, A. Naik, A. Ivanova, V. Baskaran, I. Laptev, R. Glatt, T. Ben-Nun, P. Torr, N. Jaques, A. Prabhu, B. Bartoldson, B. Kailkhura, C. Schroeder de Witt. International Conference on Machine Learning (ICML). 2026.
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.