bigalgebra
is designed to interoperate with the
bigmemory
ecosystem. This vignette demonstrates how to
create in-memory and file-backed big.matrix
objects,
interact with them via the package’s wrappers, and manage the underlying
resources safely.
big.matrix
objectsIn-memory matrices behave much like ordinary R matrices but reside in shared memory, allowing multiple R sessions to access the same data.
X <- big.matrix(3, 3, type = "double", init = 0)
X[,] <- matrix(1:9, nrow = 3)
X[]
#> [,1] [,2] [,3]
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
Once created, the objects can be passed directly to Level 1 helpers:
File-backed matrices persist their contents on disk, making them suitable for data sets that exceed available RAM.
dir.create(tmp_fb <- tempfile())
Y <- filebacked.big.matrix(4, 2, type = "double",
backingpath = tmp_fb,
backingfile = "fb.bin",
descriptorfile = "fb.desc",
init = 0)
Y[,] <- matrix(runif(8), nrow = 4)
Y[]
#> [,1] [,2]
#> [1,] 0.87248600 0.80444244
#> [2,] 0.22247946 0.42258322
#> [3,] 0.02441364 0.08168393
#> [4,] 0.14834608 0.96116514
These objects participate in higher-level operations without being loaded into memory.
Z <- filebacked.big.matrix(4, 2, type = "double",
backingpath = tmp_fb,
backingfile = "res.bin",
descriptorfile = "res.desc",
init = 0)
dvcal(ALPHA = 1.5, X = Y, BETA = 0, Y = Z)
Z[]
#> [,1] [,2]
#> [1,] 1.30872901 1.2066637
#> [2,] 0.33371919 0.6338748
#> [3,] 0.03662046 0.1225259
#> [4,] 0.22251912 1.4417477