Create a GPU-aware sparse matrix

Usage

cuda_sparse(
  x,
  format = c("csr", "coo"),
  device = c("auto", "cuda", "cpu"),
  drop_zeros = TRUE
)

Arguments

x: A numeric matrix, a sparse matrix from the Matrix package, or a cudasparse object.
format: Logical storage format, "csr" or "coo".
device: One of "auto", "cuda", or "cpu".
drop_zeros: Whether to remove explicitly stored zeros.

Value

A cudasparse list. Stable public metadata include one-based COO i and j, numeric values, zero-based CSR row_ptr and col_index, integer shape, matrix dimnames, logical format, actual device, and backend. storage is backend-internal and should not be accessed directly.

Details

Existing cudasparse inputs use their stable sorted COO mirror directly. Same-device format changes share backend storage; transfers and zero filtering do not construct an intermediate Matrix object.

Examples

library(Matrix)
x <- rsparsematrix(5, 4, density = 0.25)
cuda_sparse(x, device = "cpu")
#> <cudasparse[5x4] nnz=5 format=csr device=cpu backend=Matrix>
#> 5 x 4 sparse Matrix of class "dgCMatrix"
#>                        
#> [1,]  .    .     .    .
#> [2,]  .    .    -0.97 .
#> [3,]  .    0.69  .    .
#> [4,] -0.96 0.80  .    .
#> [5,] -0.48 .     .    .