Posts by Collection

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.

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.

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.

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.

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.

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

Teaching Assistant

Course, University of Tennessee, Department of Computer Science, 2019

Teaching assistant for Computer Science 140: Data Structures and Algorithms I.