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hhakim's avatar
Update Faust::TransformHelperPoly.
hhakim authored 
- Optimization of TransformHelperPoly::multiply(Vect): no realloc() calls, directly allocate the final vector avoiding the whole buffer copy.
- Implementation of TransformHelperPoly::multiply(Matdense)
- Optimization of TransformHelperPoly::basisChebyshev: building factors in parallel with OpenMP.
- Multiple impls of TransformHelperPoly::poly to compute linear combination of polynomials.
89024747
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hhakim's avatar 89024747
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Name Last commit Last update
CMake
externals
gen_doc
misc
src
wrapper
gpu_mod @ 94f9562a
.gitlab-ci.yml
.gitmodules
CDashConfScript.cmake
CHANGELOG.md
CMakeLists.txt
CTestConfig.cmake
README.developer
README.md
TODOLIST.developer
gettingStartedFAuST-version2_1.pdf
license.txt
license_hdf5.txt
license_matio.txt
license_zlib.txt
README.md

pipeline status FAµST Logo

FAuST Toolbox -- Flexible Approximate Multi-Layer Sparse Transform

General purpose

The FAuST toolbox contains a C++ code implementing a general framework designed to factorize matrices of interest into multiple sparse factors. It contains a template CPU/GPU C++ code and a Matlab wrapper. A Python wrapper is also available. The algorithms implemented here are described in details in [1]- Le Magoarou

For more information on the FAuST Project, please visit the website of the project: FAµST website

Installation

Please refer to the document "./gettingStartedFAuST-version2_0.pdf" to install the FAUST toolbox. The FAUST toolbox has been tested on the following environments:

  • LINUX (fedora 24 - 33 / centos 7 / Ubuntu)
  • MACOS X
  • WINDOWS (windows 10)

Quick install on UNIX

Unpack the directory.
mkdir ./build
cd ./build
cmake .. OR ccmake .. (with Graphical User Interface)
make
make install

Warning: The Matlab interface of FAuST requires compiling mex files. The mex compiler compatible with specific versions of gcc depending on the platform used. For more information, please refer to the Mathworks website.

Quickest Install on Linux, Windows and macOS

Pre-compiled packages from Gitlab Continuous Integration are also available. Except of course PIP packages, all packages include matlab and python wrappers, below are the latest release links.

License

Cf. license.txt

Contacts

Rémi Gribonval: remi.gribonval@inria.fr
Hakim: hakim.hadj-djilani@inria.fr

Credits

Luc Le Magoarou
Remi Gribonval
Nicolas Bellot
Adrien Leman
Thomas Gautrais
Hakim Hadj-Djilani

References

[1] Le Magoarou L. and Gribonval R., "Flexible multi-layer sparse approximations of matrices and applications", Journal of Selected Topics in Signal Processing, 2016.