@inproceedings{12fe243fc5ce4cf28d02af1614800769,
title = "A Portable and Efficient Lagrangian Particle Capability for Idealized Atmospheric Phenomena",
abstract = "The Cloud Model version 1 is an atmospheric model that allows for idealized studies of atmospheric phenomena. A new Lagrangian microphysics capability has been added, enabling a significantly more accurate representation than the traditional bulk or multi-moment approaches frequently found in mesoscale atmospheric models. We have utilized a directive-based approach to enable a single source code to efficiently support execution on both CPU and GPU-based computing platforms. In addition to the use of accelerator directives, changes to the data structures and the message-passing approach used by the Lagrangian particle-based microphysics module were necessary to enable efficient execution for a large number of particles. We focus on a configuration that will be used to investigate the impact of oceanic sea spray on the atmospheric boundary layer within a hurricane. We observe a factor of 5.1{\texttimes} reduction in time to the solution when comparing the execution time for 256 NVIDIA A100 GPUs versus 256 AMD EPYC{\texttrademark} Milan-based compute nodes using 1 billion particles.",
keywords = "GPU, MPI, OpenACC, power efficiency, roofline",
author = "John Dennis and Jian Sun and Sheri Voelz and George Bryan and David Richter",
note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; Platform for Advanced Scientific Computing Conference, PASC 2024 ; Conference date: 03-06-2024 Through 05-06-2024",
year = "2024",
month = jun,
day = "3",
doi = "10.1145/3659914.3659940",
language = "English",
series = "PASC 2024 - Proceedings of the Platform for Advanced Scientific Computing Conference",
publisher = "Association for Computing Machinery, Inc",
booktitle = "PASC 2024 - Proceedings of the Platform for Advanced Scientific Computing Conference",
}