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ORCIDDaniel Nichols
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2020 – today
- 2024
[c11]Arunavo Dey, Aakash Dhakal, Tanzima Z. Islam, Jae-Seung Yeom, Tapasya Patki, Daniel Nichols, Alexander Movsesyan, Abhinav Bhatele:
Relative Performance Prediction Using Few-Shot Learning. COMPSAC 2024: 1764-1769- [c10]Daniel Nichols
, Joshua Hoke Davis, Zhaojun Xie, Arjun Rajaram, Abhinav Bhatele:
Can Large Language Models Write Parallel Code? HPDC 2024: 281-294 - [c9]Daniel Nichols, Alexander Movsesyan, Jae-Seung Yeom, Abhik Sarkar, Daniel Milroy, Tapasya Patki, Abhinav Bhatele:
Predicting Cross-Architecture Performance of Parallel Programs. IPDPS 2024: 570-581 - [c8]Harshitha Menon, Daniel Nichols, Abhinav Bhatele, Todd Gamblin:
Learning to Predict and Improve Build Successes in Package Ecosystems. MSR 2024: 531-542 - [c7]Daniel Nichols, Aniruddha Marathe, Harshitha Menon, Todd Gamblin, Abhinav Bhatele:
HPC-Coder: Modeling Parallel Programs using Large Language Models. ISC 2024: 1-12 - [i6]Daniel Nichols, Joshua Hoke Davis, Zhaojun Xie, Arjun Rajaram, Abhinav Bhatele:
Can Large Language Models Write Parallel Code? CoRR abs/2401.12554 (2024) - [i5]Onur Cankur, Aditya Tomar, Daniel Nichols, Connor Scully-Allison, Katherine E. Isaacs, Abhinav Bhatele:
Automated Programmatic Performance Analysis of Parallel Programs. CoRR abs/2401.13150 (2024) - [i4]Daniel Nichols, Pranav Polasam, Harshitha Menon, Aniruddha Marathe, Todd Gamblin, Abhinav Bhatele:
Performance-Aligned LLMs for Generating Fast Code. CoRR abs/2404.18864 (2024) - 2023
- [c6]Joshua Hoke Davis, Justin Shafner, Daniel Nichols, Nathan Grube, Pino Martin, Abhinav Bhatele:
Porting a Computational Fluid Dynamics Code with AMR to Large-scale GPU Platforms. IPDPS 2023: 602-612 - [i3]Daniel Nichols, Aniruddha Marathe, Harshitha Menon, Todd Gamblin, Abhinav Bhatele:
Modeling Parallel Programs using Large Language Models. CoRR abs/2306.17281 (2023) - 2022
- [c5]Daniel Nichols, Aniruddha Marathe, Kathleen Shoga, Todd Gamblin, Abhinav Bhatele:
Resource Utilization Aware Job Scheduling to Mitigate Performance Variability. IPDPS 2022: 335-345 - 2021
- [i2]Shu-Huai Lin, Daniel Nichols, Siddharth Singh, Abhinav Bhatele:
How to Train Your Neural Network: A Comparative Evaluation. CoRR abs/2111.04949 (2021) - 2020
- [c4]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:
Integrating Deep Learning in Domain Sciences at Exascale. SMC 2020: 35-50 - [i1]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:
Integrating Deep Learning in Domain Sciences at Exascale. CoRR abs/2011.11188 (2020)
2010 – 2019
- 2019
- [c3]Daniel Nichols, Nathalie-Sofia Tomov, Frank Betancourt, Stanimire Tomov, Kwai Wong, Jack J. Dongarra:
MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing. ISC Workshops 2019: 490-503 - [c2]Frank Betancourt, Kwai Wong, Efosa Asemota, Quindell Marshall, Daniel Nichols, Stanimire Tomov:
openDIEL: A Parallel Workflow Engine and Data Analytics Framework. PEARC 2019: 20:1-20:7 - [c1]Daniel Nichols, Kwai Wong, Stanimire Tomov, Lucien Ng, Sihan Chen, Alex Gessinger:
MagmaDNN: Accelerated Deep Learning Using MAGMA. PEARC 2019: 71:1-71:6 - 2018
- [j1]Daniel Nichols:
Analogues of the 3 x + 1 Problem in Polynomial Rings of Characteristic 2. Exp. Math. 27(1): 100-110 (2018)
Coauthor Index
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