lxu 85ab466f4f | 1 year ago | |
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code | 1 year ago | |
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README.md | 1 year ago |
We deploy a cuda version for nlfff. Then we do experiments with RTX 3090, which has achieved 800x times faster speed than CPU. The gpu-memory should be 4x times larger than the size of output 3D data.
run code/use.ipynb script, which include:
This repository is a development repository, see
code/use.ipynb
for a usage exampleSubsequent major releases will also be merged into the toolkit pynlfff.
The original CPU version code was Wiegelmann(wiegelmann@mps.mpg.de
) NLFFF extrapolation algorithm
@article{Wiegelmann2012Nov,
author = {Wiegelmann, T. and Thalmann, J. K. and Inhester, B. and Tadesse, T. and Sun, X. and Hoeksema, J. T.},
title = {{How Should One Optimize Nonlinear Force-Free Coronal Magnetic Field Extrapolations from SDO/HMI Vector Magnetograms?}},
journal = {Sol. Phys.},
volume = {281},
number = {1},
pages = {37--51},
year = {2012},
month = nov,
issn = {1573-093X},
publisher = {Springer Netherlands},
doi = {10.1007/s11207-012-9966-z}
}
The GPU version of NLFFF was here
@misc{Zhang2022,
author = {Xinze, Zhang},
title = {The cuda version for nlfff},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/deepsolar/nlfff_gpu}},
commit = {83d83f6ade2bc0eed429e1f90ee39636460bbedc}
}
GPU Author: Xinze Zhang (zhangxinze17@mails.ucas.ac.cn
)
Community: https://gitter.im/deepsolar/pynlfff
No Description
C Jupyter Notebook Cuda Python C++ other
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