Bulletin of the American Physical Society
APS April Meeting 2024
Wednesday–Saturday, April 3–6, 2024; Sacramento & Virtual
Session KK03: V: Mini-Symposium: Cosmology and Galaxies
11:00 AM–1:00 PM,
Saturday, April 6, 2024
Room: Virtual Room 03
Sponsoring
Unit:
DAP
Chair: Xiaofeng Dong, The University of Chicago
Abstract: KK03.00006 : Benchmarking AI-evolved cosmological structure formation and expanding dimensions through parallelization frameworks
12:24 PM–12:36 PM
Presenter:
Xiaofeng Dong
(The University of Chicago)
Authors:
Xiaofeng Dong
(The University of Chicago)
Nesar Ramachandra
(Argonne National Laboratory)
Azton Wells
(Argonne National Laboratory)
Michael Buehlmann
(Argonne National Laboratory)
Salman Habib
(Argonne National Laboratory)
Katrin Heitmann
(Argonne National Laboratory)
In addition, we develop strategies to expand the available dynamical range of deep neural networks' output. While conventional studies often showcase the neural network predictions within modest spatial dimensions and particle counts, their practical use requires significantly larger box sizes and number of particles to yield physically relevant predictions for cosmological structure formation. To achieve this, we harness data and model parallelism frameworks during model training, and a split-and-recombine approach at model deployment for the large simulation box. We are able to achieve box sizes multiple times larger than what is typically found in the existing literature. This opens up the possibility of overcoming a number of computational bottlenecks.
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