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rkeops: Kernel Operations on GPU or CPU, 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 with CPU and GPU computing support. It combines a tiled reduction scheme with an automatic differentiation engine. It is perfectly suited to the efficient computation of Kernel dot products and the associated gradients, even when the full kernel matrix does not fit into the GPU memory.
Getting started
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Package details |
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| Author | 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') |
| Maintainer | Ghislain Durif <gd.dev@libertymail.net> |
| License | MIT + file LICENSE |
| Version | 1.4.2.2 |
| URL | https://www.kernel-operations.io/ https://github.com/getkeops/keops/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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