Skip to main content

divERGe implements various ERG examples

Project description

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/

Download CPU release

Generic linux (amd64) builds (GLIBC>=2.17, this should be given almost anywhere to date) can be downloaded here. 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 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 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. The releases include differently licensed software (OpenBLAS, FFTW) in binary form.

Authors

Jonas B. Profe and Lennart Klebl, 2024.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

diverge_flow-0.8.3.tar.gz (2.6 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

diverge_flow-0.8.3-py3-none-any.whl (2.6 MB view details)

Uploaded Python 3

File details

Details for the file diverge_flow-0.8.3.tar.gz.

File metadata

  • Download URL: diverge_flow-0.8.3.tar.gz
  • Upload date:
  • Size: 2.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.13.1

File hashes

Hashes for diverge_flow-0.8.3.tar.gz
Algorithm Hash digest
SHA256 7bed29498cc9917e7408805103e5690d1af788097a51f032684bb8f5fa27a94b
MD5 3229256d0dd65d77ee0b0c653c9e0957
BLAKE2b-256 4e4e4eaa3147188b6004c69b8ec59aefc0a248ae7981f0cc8261626d5f460dc2

See more details on using hashes here.

File details

Details for the file diverge_flow-0.8.3-py3-none-any.whl.

File metadata

  • Download URL: diverge_flow-0.8.3-py3-none-any.whl
  • Upload date:
  • Size: 2.6 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.13.1

File hashes

Hashes for diverge_flow-0.8.3-py3-none-any.whl
Algorithm Hash digest
SHA256 b8ee4dbb294a591c2f87b4cda35b7df5478f141c2709772dd38aee6adf1ebc62
MD5 3548f4ccf3c940fbc70268cbaa44f81d
BLAKE2b-256 5317ae82d28b755255e96bf28db78bde0a2a317d04c7e20c8080f5efd548f853

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page