RKeOps contains the R bindings for the cpp/cuda library KeOps. It provides standard R functions that can be used in any R (>=3) codes.

For a full documentation you may read:

• Installation
• Documentation
• Learning KeOps syntax with examples
• Tutorials gallery

Authors

Feel free to contact us for any bug report or feature request, you can also fill an issue report on GitHub.

KeOps, PyKeOps, KeOpsLab

• Benjamin Charlier
• Jean Feydy
• Joan Alexis Glaunès

RKeOps

• Ghislain Durif

Contributors

• François-David Collin

Details

The KeOps library provides seamless kernel operations on GPU, with auto-differentiation and without memory overflows.

With RKeOps, you can 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. 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.

For more information (installation, usage), please visit https://www.kernel-operations.io/ (especially the section dedicated to RKeOps) and read the vignettes available in R with the command browseVignettes("rkeops") or on the CRAN.

Installation

SystemRequirements

• C++11: you should have a C++ compiler compatible with C++11 (see https://en.cppreference.com/w/cpp/compiler_support for details). We recommend GCC or clang.
• cmake (>=3.10): should be available in standard path. You can get cmake at https://cmake.org/.
• clang (optional): clang compiler is supported.
• CUDA (optional but recommended for performance): you need CUDA to enable GPU computing in RKeOps. CUDA can be found at https://developer.nvidia.com/cuda-downloads.
• At the moment, RKeOps is not available for Windows.

Install from CRAN

Note: RKeOps is avaible on CRAN but only for UNIX environment (GNU/Linux and MacOS) and not for Windows.

install.packages("rkeops")

Install from sources

!! In most recent version of devtools, the args argument is not available anymore and it is not possible to use devtools::install_git. Please check next section to install from sources.

• Install directly from Github (requires git)

devtools::install_git("https://github.com/getkeops/keops", 
                      subdir = "rkeops", 
                      args="--recursive")
# not possible to use `devtools::intall_github()` because of the required submodule

Get sources and install from local repository

• Get KeOps sources (bash command)

git clone --recurse-submodules="keops/lib/sequences" https://github.com/getkeops/keops
# or
git clone https://github.com/getkeops/keops
cd keops
git submodule update --init -- keops/lib/sequences
# other submodules are not necessary for RKeOps

• Install from local source in R (assuming you are in the keops directory)

devtools::install("rkeops")

Quick start

Defining a new operator

Here is an example how to define and compute a Gaussian convolution with RKeOps.

# implementation of a convolution with a Gaussian kernel
formula = "Sum_Reduction(Exp(-s * SqNorm2(x - y)) * b, 0)"

# input arguments
args = c("x = Vi(3)",      # vector indexed by i (of dim 3)
         "y = Vj(3)",      # vector indexed by j (of dim 3)
         "b = Vj(6)",      # vector indexed by j (of dim 6)
         "s = Pm(1)")      # parameter (scalar)

# compilation of the corresponding operator
op <- keops_kernel(formula, args)

# data and parameter values
nx <- 100
ny <- 150
X <- matrix(runif(nx*3), nrow=nx)   # matrix 100 x 3
Y <- matrix(runif(ny*3), nrow=ny)   # matrix 150 x 3
B <- matrix(runif(ny*6), nrow=ny)   # matrix 150 x 6
s <- 0.2

# to run computation on CPU (default mode)
use_cpu()
# to run computations on GPU (to be used only if relevant)
use_gpu()

# computation (order of the input arguments should be similar to `args`)
res <- op(list(X, Y, B, s))

Computing gradients

Here is an example how to define and compute the gradient of an existing KeOps operators.

# defining an operator (reduction on squared distance)
formula <- "Sum_Reduction(SqNorm2(x-y), 0)"
args <- c("x=Vi(0,3)", "y=Vj(1,3)")
op <- keops_kernel(formula, args)
# defining its gradient regarding x
grad_op <- keops_grad(op, var="x")

# data
nx <- 100
ny <- 150
x <- matrix(runif(nx*3), nrow=nx, ncol=3)     # matrix 100 x 3
y <- matrix(runif(ny*3), nrow=ny, ncol=3)     # matrix 150 x 3
eta <- matrix(runif(nx*1), nrow=nx, ncol=1)   # matrix 100 x 1

# computation
input <- list(x, y, eta)
res <- grad_op(input)

CPU and GPU computing

Based on your formulae, RKeOps compile on the fly operators that can be used to run the corresponding computations on CPU or GPU, it uses a tiling scheme to decompose the data and avoid (i) useless and costly memory transfers between host and GPU (performance gain) and (ii) memory overflow.

Note: You can use the same code (i.e. define the same operators) for CPU or GPU computing. The only difference will be the compiler used for the compilation of your operators (upon the availability of CUDA on your system).

To use CPU computing mode, you can call use_cpu() (with an optional argument ncore specifying the number of cores used to run parallel computations).

To use GPU computing mode, you can call use_gpu() (with an optional argument device to choose a specific GPU id to run computations).