On 12/5/24 14:21, Luc De Wilde wrote:
Dear package developers,
in creating a package lavaanC for use in lavaan, I need to perform some matrix
computations involving matrix products and crossproducts. As far as I see I
cannot directly call the C code in the R core. So I copied the code in the R
core, but the same C/C++ code in a package is 2.5 à 3 times slower than
executed directly in R :
C code in package :
SEXP prod0(SEXP mat1, SEXP mat2) {
SEXP u1 = Rf_getAttrib(mat1, R_DimSymbol);
int m1 = INTEGER(u1)[0];
int n1 = INTEGER(u1)[1];
SEXP u2 = Rf_getAttrib(mat2, R_DimSymbol);
int m2 = INTEGER(u2)[0];
int n2 = INTEGER(u2)[1];
if (n1 != m2) Rf_error("matrices not conforming");
SEXP retval = PROTECT(Rf_allocMatrix(REALSXP, m1, n2));
double* left = REAL(mat1);
double* right = REAL(mat2);
double* ret = REAL(retval);
double werk = 0.0;
for (int j = 0; j < n2; j++) {
for (int i = 0; i < m1; i++) {
werk = 0.0;
for (int k = 0; k < n1; k++) werk += (left[i + m1 * k] * right[k + m2
* j]);
ret[j * m1 + i] = werk;
}
}
UNPROTECT(1);
return retval;
}
Test script :
m1 <- matrix(rnorm(300000), nrow = 60)
m2 <- matrix(rnorm(300000), ncol = 60)
print(microbenchmark::microbenchmark(
m1 %*% m2, .Call("prod0", m1, m2), times = 100
))
Result on my pc:
Unit: milliseconds
expr min lq mean median uq max neval
m1 %*% m2 10.5650 10.8967 11.13434 10.9449 11.02965 15.8397 100
.Call("prod0", m1, m2) 29.3336 30.7868 32.05114 31.0408 33.85935 45.5321 100
Can anyone explain why the compiled code in the package is so much slower than
in R core?
By default, R would use BLAS, not the simple algorithm above. See
?options, look for "matprod" for more information on how to select an
algorithm. The code is then in array.c, function matprod().
and
Is there a way to improve the performance in R package?
One option is to use BLAS.
Best
Tomas
Best regards,
Luc De Wilde
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