A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks
D. Nichols, S. Singh, S.-H. Lin, en A. Bhatele, “A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks”, arXiv [cs.LG]. 2022.
Publications
Resource Utilization Aware Job Scheduling to Mitigate Performance Variability
D. Nichols, A. Marathe, K. Shoga, T. Gamblin, A. Bhatele, “Resource Utilization Aware Job Scheduling to Mitigate Performance Variability”, International Parallel & Distributed Processing Symposium (IPDPS). 2022.
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, (2020)
openDIEL: A Parallel Workflow Engine And Data Analytics Framework
Frank Betancourt, Kwai Wong, Efosa Asemota, Quindell Marshall, Daniel Nichols, and Stanimire Tomov. 2019. openDIEL: A Parallel Workflow Engine and Data Analytics Framework. In Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC 19). ACM, New York, NY, USA, Article 20, 7 pages. DOI: https://doi.org/10.1145/3332186.3333051
MagmaDNN: Accelerated Deep Learning Using Magma
Daniel Nichols, Kwai Wong, Stan Tomov, Lucien Ng, Sihan Chen, and Alex Gessinger. 2019. MagmaDNN: Accelerated Deep Learning Using MAGMA. In Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC 19). ACM, New York, NY, USA, Article 71, 6 pages. DOI: https://doi.org/10.1145/3332186.3333047
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, "MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing", ISC. 2019.