# DivERGe implements various ERG examples
DivERGe provides a versatile framework to set up (one,) two and three
dimensional functional renormalization group (FRG/ERG) calculations under the
static vertex approximation.
It implements three backends, the grid FRG, truncated unity FRG (TUFRG) and
orbital space n-patch FRG.
For maximum performance, the code is written in C/C++ with extensions in CUDA
(GPUs). It makes minimal use of other dependencies, only FFTW and LAPACK are
required. MPI may be used if desired. DivERGe can be interfaced from C/C++ or
python, with an existing python FFI wrapper. This wrapper is published in pypi,
such that you can run
```
pip install diverge-flow
```
on a 64bit linux machine and directly use divERGe. For different architectures,
compilation is additionally required (and putting the correct
```libdivERGe.so``` in your ```LD_LIBRARY_PATH```). You can verify the .so file
in use by calling ```diverge.info()``` from python. For any other language, you
must write all the FFI wrappers yourself.
# [Documentation](https://frg.pages.rwth-aachen.de/diverge/)
[https://frg.pages.rwth-aachen.de/diverge/](https://frg.pages.rwth-aachen.de/diverge/)
# [Download CPU release](https://git.rwth-aachen.de/frg/diverge/-/raw/master/public/releases/v0.4/divERGe.tar.gz)
Generic linux (amd64) builds (GLIBC>=2.17, this should be given almost anywhere
to date) can be downloaded
[here](https://git.rwth-aachen.de/frg/diverge/-/tree/master/public/releases). We
recommend building from source for an optimized version on the HPC
infrastructure to your availability.
# Testing
We use a slightly modified version of
[Catch2](https://github.com/catchorg/Catch2) for testing. To check divERGe's
health from python, run
```
import diverge
diverge.init(None, None)
diverge.run_tests()
diverge.finalize()
```
# Citation
Please cite [this paper](https://doi.org/10.21468/SciPostPhysCodeb.26) when
using divERGe for your work. You may use the following BibTex entry:
```
@Article{10.21468/SciPostPhysCodeb.26,
title={{divERGe implements various Exact Renormalization Group examples}},
author={Jonas B. Profe and Dante M. Kennes and Lennart Klebl},
journal={SciPost Phys. Codebases},
pages={26},
year={2024},
publisher={SciPost},
doi={10.21468/SciPostPhysCodeb.26},
url={https://scipost.org/10.21468/SciPostPhysCodeb.26},
}
```
# License
divERGe is published under the
[GPLv3](https://www.gnu.org/licenses/gpl-3.0.html). The releases include
differently licensed software ([OpenBLAS](https://www.openblas.net/),
[FFTW](https://www.fftw.org/)) in binary form.
<!-- non-free parts ([CUDA](https://developer.nvidia.com/cuda-toolkit)) and -->
# Authors
**Jonas B. Profe** and **Lennart Klebl**, 2024.
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"description": "# DivERGe implements various ERG examples\nDivERGe provides a versatile framework to set up (one,) two and three\ndimensional functional renormalization group (FRG/ERG) calculations under the\nstatic vertex approximation.\n\nIt implements three backends, the grid FRG, truncated unity FRG (TUFRG) and\norbital space n-patch FRG.\n\nFor maximum performance, the code is written in C/C++ with extensions in CUDA\n(GPUs). It makes minimal use of other dependencies, only FFTW and LAPACK are\nrequired. MPI may be used if desired. DivERGe can be interfaced from C/C++ or\npython, with an existing python FFI wrapper. This wrapper is published in pypi,\nsuch that you can run\n```\npip install diverge-flow\n```\non a 64bit linux machine and directly use divERGe. For different architectures,\ncompilation is additionally required (and putting the correct\n```libdivERGe.so``` in your ```LD_LIBRARY_PATH```). You can verify the .so file\nin use by calling ```diverge.info()``` from python. For any other language, you\nmust write all the FFI wrappers yourself.\n\n# [Documentation](https://frg.pages.rwth-aachen.de/diverge/)\n[https://frg.pages.rwth-aachen.de/diverge/](https://frg.pages.rwth-aachen.de/diverge/)\n\n# [Download CPU release](https://git.rwth-aachen.de/frg/diverge/-/raw/master/public/releases/v0.4/divERGe.tar.gz)\nGeneric linux (amd64) builds (GLIBC>=2.17, this should be given almost anywhere\nto date) can be downloaded\n[here](https://git.rwth-aachen.de/frg/diverge/-/tree/master/public/releases). We\nrecommend building from source for an optimized version on the HPC\ninfrastructure to your availability.\n\n# Testing\nWe use a slightly modified version of\n[Catch2](https://github.com/catchorg/Catch2) for testing. To check divERGe's\nhealth from python, run\n```\nimport diverge\ndiverge.init(None, None)\ndiverge.run_tests()\ndiverge.finalize()\n```\n\n# Citation\nPlease cite [this paper](https://doi.org/10.21468/SciPostPhysCodeb.26) when\nusing divERGe for your work. You may use the following BibTex entry:\n```\n@Article{10.21468/SciPostPhysCodeb.26,\n\ttitle={{divERGe implements various Exact Renormalization Group examples}},\n\tauthor={Jonas B. Profe and Dante M. Kennes and Lennart Klebl},\n\tjournal={SciPost Phys. Codebases},\n\tpages={26},\n\tyear={2024},\n\tpublisher={SciPost},\n\tdoi={10.21468/SciPostPhysCodeb.26},\n\turl={https://scipost.org/10.21468/SciPostPhysCodeb.26},\n}\n```\n\n# License\ndivERGe is published under the\n[GPLv3](https://www.gnu.org/licenses/gpl-3.0.html). The releases include\ndifferently licensed software ([OpenBLAS](https://www.openblas.net/),\n[FFTW](https://www.fftw.org/)) in binary form.\n<!-- non-free parts ([CUDA](https://developer.nvidia.com/cuda-toolkit)) and -->\n\n# Authors\n**Jonas B. Profe** and **Lennart Klebl**, 2024.\n",
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