Kernel Operations on the GPU, with Autodiff, without Memory Overflows

The 'KeOps' library lets you compute generic reductions of very large arrays whose entries are given by a mathematical formula. It combines a tiled reduction scheme with an automatic differentiation engine, and can be used through 'R', 'Matlab', 'NumPy' or 'PyTorch' backends. It is perfectly suited to the computation of Kernel dot products and the associated gradients, even when the full kernel matrix does not fit into the GPU memory.


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Reference manual

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install.packages("rkeops")

1.4.1.1 by Ghislain Durif, 2 months ago


https://www.kernel-operations.io/, https://github.com/getkeops/keops/


Report a bug at https://github.com/getkeops/keops/issues


Browse source code at https://github.com/cran/rkeops


Authors: Benjamin Charlier [aut] (<http://imag.umontpellier.fr/~charlier/>) , Jean Feydy [aut] (<https://www.math.ens.fr/~feydy/>) , Joan A. Glaunès [aut] (<https://www.mi.parisdescartes.fr/~glaunes/>) , Ghislain Durif [aut, cre] (<https://gdurif.perso.math.cnrs.fr/>) , François-David Collin [ctb] (Development-related consulting and support) , Daniel Frey [ctb] (Author of the included C++ library 'sequences')


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports Rcpp, openssl, stringr

Suggests testthat, knitr, rmarkdown

Linking to Rcpp, RcppEigen

System requirements: C++11, cmake (>= 3.10), clang (optional), CUDA (optional but recommended)


See at CRAN