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Beyond its Level 1 vector helpers, bigalgebra includes
wrappers around the Level 3 BLAS routines and related utilities for
manipulating matrices stored in memory or as
[bigmemory::big.matrix] objects. This vignette demonstrates
the core workflows for the symmetric matrix-matrix multiply
dsymm(), the general matrix multiply dgemm()
and the AXPY-style updater daxpy().
dsymm()dsymm() mirrors the BLAS symmetric matrix-matrix
multiplication kernel. It can multiply from the left or right and
accepts optional leading-dimension arguments when working with
non-standard strides.
A_sym <- matrix(c(2, 1, 1, 3), 2, 2)
B_rhs <- diag(2)
C_out <- matrix(0, 2, 2)
dsymm(A = A_sym, B = B_rhs, C = C_out, SIDE = "L", UPLO = "U")
C_out
#> [,1] [,2]
#> [1,] 2 1
#> [2,] 1 3When either input is a big.matrix, the wrapper performs
the computation in place without materialising an intermediate R
matrix.
library(bigmemory)
dir.create(tmp_sym <- tempfile())
A_bm <- filebacked.big.matrix(2, 2, type = "double",
backingpath = tmp_sym,
backingfile = "A.bin",
descriptorfile = "A.desc")
A_bm[,] <- A_sym
B_bm <- filebacked.big.matrix(2, 2, type = "double",
backingpath = tmp_sym,
backingfile = "B.bin",
descriptorfile = "B.desc")
B_bm[,] <- B_rhs
C_bm <- filebacked.big.matrix(2, 2, type = "double",
backingpath = tmp_sym,
backingfile = "C.bin",
descriptorfile = "C.desc")
dsymm(A = A_bm, B = B_bm, C = C_bm)
C_bm[]
#> [,1] [,2]
#> [1,] 2 1
#> [2,] 1 3dgemm()dgemm() exposes the workhorse Level 3 BLAS routine that
computes C := alpha * op(A) %*% op(B) + beta * C. All
matrices can be ordinary R matrices or big.matrix objects,
and any missing output will be created automatically. When you construct
native R matrices, coerce integer literals to doubles (for example with
as.numeric()) so they satisfy the package’s
double-precision requirement.
A <- matrix(as.numeric(1:6), nrow = 2)
B <- matrix(seq(2, 12, by = 2), nrow = 3)
C <- matrix(0, nrow = 2, ncol = 4)
dgemm(TRANSA = "N", TRANSB = "N", A = A, B = B, C = C, BETA = 0)
C
#> [,1] [,2] [,3] [,4]
#> [1,] 44 98 0 0
#> [2,] 56 128 0 0Transposed operands are also supported via TRANSA and
TRANSB:
daxpy()daxpy() provides a convenient wrapper for the AXPY
operation Y := alpha * X + Y. The helper respects the
package option bigalgebra.mixed_arithmetic_returns_R_matrix
to decide when to return a native matrix versus a
big.matrix.
X <- matrix(1, nrow = 2, ncol = 2)
Y <- matrix(c(0, 1, 2, 3), nrow = 2)
daxpy(A = 0.5, X = X, Y = Y)
#> An object of class "big.matrix"
#> Slot "address":
#> <pointer: 0x11095e9c0>When both operands are big.matrix objects, the result
stays on disk:
dir.create(tmp_axpy <- tempfile())
X_bm <- filebacked.big.matrix(2, 2, type = "double",
backingpath = tmp_axpy,
backingfile = "X.bin",
descriptorfile = "X.desc")
X_bm[,] <- 1
daxpy(A = 3, X = X_bm)
#> An object of class "big.matrix"
#> Slot "address":
#> <pointer: 0x110971160>
X_bm[]
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 1 1These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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