use_gpu

integer, GPU device id to be used for computations. Default
is <code>0</code>. It is recommended to use default GPU and manage GPU assignment
outside R by setting the environment variable <code>CUDA_VISIBLE_DEVICES</code>.

device

Set up <code>rkeops</code> runtime options to use GPU computing when calling
user-defined operators.

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.

Ghislain Durif

rkeops

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

Benjamin Charlier

Jean Feydy

Joan A. Glaunès

François-David Collin

Daniel Frey

use_gpu function

<dl><dt>device</dt>
<dd>integer, GPU device id to be used for computations. Default
is <code>0</code>. It is recommended to use default GPU and manage GPU assignment
outside R by setting the environment variable <code>CUDA_VISIBLE_DEVICES</code>.</dd></dl>

Arguments

Author

Enable GPU-computing when calling user-defined operators — use_gpu

<dl>

<dt>device</dt>
<dd>integer, GPU device id to be used for computations. Default
is <code>0</code>. It is recommended to use default GPU and manage GPU assignment
outside R by setting the environment variable <code>CUDA_VISIBLE_DEVICES</code>.</dd>

</dl>

use_gpu: Enable GPU-computing when calling user-defined operators

Description

Usage

Value

Arguments

Author

Details

See Also

Examples