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- Creator:
- An, Yifu
- Description:
- We have ported our MHD code, BATSRUS ( https://github.com/SWMFsoftware/BATSRUS), to the GPU. This dataset contains the input parameters and raw timing results for the Paper. To reproduce the results, please follow the instructions and use the software specifications contained in readme.txt. and Abstract: BATSRUS, our state-of-the-art extended magnetohydrodynamic code, is the most used and one of the most resource-consuming models in the Space Weather Modeling Framework. It has always been our objective to improve its efficiency and speed with emerging techniques, such as GPU acceleration. To utilize the GPU nodes on modern supercomputers, we port BATSRUS to GPUs with the OpenACC API. Porting the code to a single GPU requires rewriting and optimizing the most used functionalities of the original code into a new solver, which accounts for around 1% of the entire program in length. To port it to multiple GPUs, we implement a new message passing algorithm to support its unique block-adaptive grid feature. We conduct weak scaling tests on as many as 256 GPUs and find good performance. The program has 50-60% parallel efficiency on up to 256 GPUs, and up to 95% efficiency within a single node (4 GPUs). Running large problems on more than one node has reduced efficiency due to hardware bottlenecks. We also demonstrate our ability to run representative magnetospheric simulations on GPUs. The performance for a single A100 GPU is about the same as 270 AMD "Rome" CPU cores, and it runs 3.6 times faster than real time. The simulation can run 6.9 times faster than real time on four A100 GPUs.
- Keyword:
- BATSRUS, GPU, and MHD simulation
- Citation to related publication:
- An, Y., Chen, Y., Zhou, H., Gaenko, A. and Toth, G. (2024). BATSRUS GPU: Faster than Real Time Magnetospheric Simulations with a Block Adaptive Grid Code. Being revised. A preprint is available at http://arxiv.org/abs/2501.06717.
- Discipline:
- Engineering
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- Creator:
- Jia, Xianzhe
- Description:
- Text files containing simulated magnetic field vectors in Ganymede-centered spherical (GSPH) coordinates along the trajectories of Galileo and Juno close flybys of Ganymede. Each data file contains columns of the event time (date + hr:min:sec), the radial (Br), polar (Bt) and azimuthal (Bp) components of the simulated magnetic field, except for the G28 flyby output, which contains multiple columns of the simulated magnetic field vectors from simulation time steps of 70 to 131.
- Keyword:
- Ganymede, MHD simulation, Magnetic field, Galileo, and Juno
- Citation to related publication:
- Jia, X., M. G. Kivelson, K. K. Khurana, and R. J. Walker (2024), Improved models of Ganymede’s intrinsic and induced magnetic fields based on Galileo and Juno data, Journal of Geophysical Research – Planets, doi:10.1029/2024JE008309, in revision.
- Discipline:
- Science
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- Creator:
- Hill, Shannon C.
- Description:
- This dataset represents a theta aurora case study in which we simulated a specific theta aurora event observed by IMAGE spacecraft on 15 May 2005. The IMAGE observations and the Wind solar wind observations can be found via cdaweb. We simulate eight hours of the event with SWMF Geospace on the Great Lakes HPC cluster (15 May 2005 0020 UT - 0820 UT). We use the log output files to plot the simulation SYM-H and AL indices using a python script. We use the IDL output files to plot the IE simulation results - we focus on timesteps 0531 UT, 0539 UT, and 0602 UT - using the same python script. The BATSRUS portion (MHD) of the simulation framework produces 3D output files (extension .dat) in ascii. We use Tecplot 360 2021 R2 preplot script to convert the 3D output files into binary (extension .plt) for analysis. We use Tecplot 360 2021 R2 software to view the 3D output files in the Tecplot 360 2021 R2 GUI. We focus the analysis of the 3D output files on timesteps 0531 UT and 0602 UT. We use a python script to extract information from the 3D output files and plot in 2D with the IE data.
- Keyword:
- magnetosphere, aurora, and MHD simulation
- Citation to related publication:
- https://doi.org/10.1029/2023GL108002
- Discipline:
- Science