Daniel Nichols

According to our database, Daniel Nichols authored at least 16 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Performance-Aligned LLMs for Generating Fast Code.

Daniel Nichols, Pranav Polasam, Harshitha Menon, Aniruddha Marathe, Todd Gamblin, Abhinav Bhatele

CoRR, 2024

Automated Programmatic Performance Analysis of Parallel Programs.

Onur Cankur, Aditya Tomar, Daniel Nichols, Connor Scully-Allison, Katherine E. Isaacs, Abhinav Bhatele

CoRR, 2024

HPC-Coder: Modeling Parallel Programs using Large Language Models.

Daniel Nichols, Aniruddha Marathe, Harshitha Menon, Todd Gamblin, Abhinav Bhatele

Proceedings of the ISC High Performance 2024 Research Paper Proceedings (39th International Conference), 2024

Learning to Predict and Improve Build Successes in Package Ecosystems.

Harshitha Menon, Daniel Nichols, Abhinav Bhatele, Todd Gamblin

Proceedings of the 21st IEEE/ACM International Conference on Mining Software Repositories, 2024

Predicting Cross-Architecture Performance of Parallel Programs.

Daniel Nichols, Alexander Movsesyan, Jae-Seung Yeom, Abhik Sarkar, Daniel Milroy, Tapasya Patki, Abhinav Bhatele

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2024

Can Large Language Models Write Parallel Code?

Daniel Nichols, Joshua Hoke Davis, Zhaojun Xie, Arjun Rajaram, Abhinav Bhatele

Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing, 2024

Relative Performance Prediction Using Few-Shot Learning.

Arunavo Dey, Aakash Dhakal, Tanzima Z. Islam, Jae-Seung Yeom, Tapasya Patki, Daniel Nichols, Alexander Movsesyan, Abhinav Bhatele

Proceedings of the 48th IEEE Annual Computers, Software, and Applications Conference, 2024

2023

Modeling Parallel Programs using Large Language Models.

Daniel Nichols, Aniruddha Marathe, Harshitha Menon, Todd Gamblin, Abhinav Bhatele

CoRR, 2023

Porting a Computational Fluid Dynamics Code with AMR to Large-scale GPU Platforms.

Joshua Hoke Davis, Justin Shafner, Daniel Nichols, Nathan Grube, Pino Martin, Abhinav Bhatele

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

2022

Resource Utilization Aware Job Scheduling to Mitigate Performance Variability.

Daniel Nichols, Aniruddha Marathe, Kathleen Shoga, Todd Gamblin, Abhinav Bhatele

Proceedings of the 2022 IEEE International Parallel and Distributed Processing Symposium, 2022

2021

How to Train Your Neural Network: A Comparative Evaluation.

Shu-Huai Lin, Daniel Nichols, Siddharth Singh, Abhinav Bhatele

CoRR, 2021

2020

Integrating Deep Learning in Domain Sciences at Exascale.

Rick Archibald, Edmond Chow, Eduardo F. D'Azevedo, Jack J. Dongarra, Markus Eisenbach, Rocco Febbo, Florent Lopez, Daniel Nichols, Stanimire Tomov, Kwai Wong, Junqi Yin

Proceedings of the Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, 2020

2019

MagmaDNN: Accelerated Deep Learning Using MAGMA.

Daniel Nichols, Kwai Wong, Stanimire Tomov, Lucien Ng, Sihan Chen, Alex Gessinger

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning), 2019

openDIEL: A Parallel Workflow Engine and Data Analytics Framework.

Frank Betancourt, Kwai Wong, Efosa Asemota, Quindell Marshall, Daniel Nichols, Stanimire Tomov

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning), 2019

MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing.

Daniel Nichols, Nathalie-Sofia Tomov, Frank Betancourt, Stanimire Tomov, Kwai Wong, Jack J. Dongarra

Proceedings of the High Performance Computing, 2019

2018

Analogues of the 3 x + 1 Problem in Polynomial Rings of Characteristic 2.

Daniel Nichols

Exp. Math., 2018