CRAN/E | rkeops

rkeops

Kernel Operations on GPU or CPU, with Autodiff, without Memory Overflows

Installation

About

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.

www.kernel-operations.io/rkeops/
github.com/getkeops/keops/
System requirements Python (>= 3.5.0), C++ compiler (gcc/clang), CUDA (optional but recommended)
Bug report File report

Key Metrics

Version 2.2.2
Published 2024-02-12 85 days ago
Needs compilation? no
License MIT
License File
CRAN checks rkeops results
OS unix

Downloads

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Maintainer

Maintainer

Ghislain Durif

gd.dev@libertymail.net

Authors

Ghislain Durif

aut / cre

(<https://gdurif.perso.math.cnrs.fr/>)

Amelie Vernay

aut

(amelie.vernay@umontpellier.fr)

Chloe Serre-Combe

aut

(chloe.serre-combe@umontpellier.fr)

Benjamin Charlier

aut

(<http://imag.umontpellier.fr/~charlier/>)

Jean Feydy

aut

(<https://www.jeanfeydy.com>)

Joan A. Glaunès

aut

(<https://www.mi.parisdescartes.fr/~glaunes/>)

François-David Collin

ctb

Material

README
Reference manual
Package source

Vignettes

Kernel Interpolation with RKeOps
RKeOps LazyTensor
Introduction to RKeOps
Using RKeOps

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Old Sources

rkeops archive

Imports

checkmate
data.table
future
lifecycle
Rdpack
reticulate
RhpcBLASctl
stats
stringi
stringr
tibble
utils

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