Thanks to all help, I finally got two (!) solutions for my problem :
Unit: milliseconds
expr min lq mean median
uq max neval
m1 %*% m2 11.2685 11.48595 11.83029 11.60745
11.83170 17.2381 200
.Call("prod0", m1, m2, PACKAGE = "ldwTest") 10.8301 11.03360 11.43360 11.18950
11.36395 24.4530 200
.Call("prod2", m1, m2, PACKAGE = "ldwTest") 10.7453 10.96310 11.29727 11.09395
11.31465 17.3467 200
m& %*% m2 : R matrix product
prod0 : the BLAS fortran GEMM routine rewritten in C++ (there was an important
rearrangement of the for loops to improve cache use)
prod1 : call, in C++, of the BLAS fortran GEMM routine
Luc
Van: Avraham Adler
Verzonden: vrijdag 6 december 2024 8:46
Aan: Luc De Wilde
CC: Dirk Eddelbuettel ; Yves Rosseel ;
r-package-devel@r-project.org
Onderwerp: Re: [R-pkg-devel] Cannot create C code with acceptable performance
with respect to internal R command.
For future reference and completeness, since I responded off list, I simply
pointed out to Luke an example of using R’s BLAS interface with DGEMV. He needs
DGEMM, but the idea is the same.
< https://github.com/aadler/minimaxApprox/blob/master/src/Chebyshev.c>
Avi
Sent from my iPhone
On Dec 6, 2024, at 12:14 AM, Luc De Wilde wrote:
Dirk,
that's indeed an easy way to go, but I'm searching for methods that doesn't
need to add other dependencies in my package, so the answer of Avraham is the
most relevant for me.
But off course, thank you for your help!
Luc
Van: Dirk Eddelbuettel
Verzonden: donderdag 5 december 2024 15:09
Aan: Luc De Wilde
CC: Tomas Kalibera ; r-package-devel@r-project.org
; Yves Rosseel
Onderwerp: Re: [R-pkg-devel] Cannot create C code with acceptable performance
with respect to internal R command.
Luc,
As Tomas mentioned, matrix-multiplication can take advantage of multiple
threads, and the 'text book' nexted loops do not do that. Now, one
alternative that appeals a lot to me is to farm out to Armadillo which also
calls LAPACK for you (as R does). And via RcppArmadillo, the setup becomes a
one-liner with the expression 'mat1 * mat2' where '*' is overloaded
appropriately (as is matrix multiplication '%*%' in R). I include your
example as self-contained and reproducible script below, on my not-so-recent
machine with twelve cores I get
$ Rscript luc.r
Unit: microseconds
expr min lq mean median uq max neval cld
C 29010.538 39242.004 47948.98 50930.500 52715.30 81668.53 100 a
R 685.658 800.653 1984.17 1129.754 2719.88 8420.66 100 b
Cpp 401.182 444.164 1775.03 651.023 1656.24 30369.15 100 b
$
but what really shines (in my eyes) is that a function
arma::mat cppprod(const arma::mat& m1, const arma::mat& m2) {
return m1 * m2;
}
gets set-up for you with no worries whatsoever and outscores the R
version. (And if you look into the Rcpp docs you can learn to make this a
little faster still but skipping a (generally recommended !!) handshake with
RNG status etc).
But different strokes for different folks, not everybody likes C++ (which is
both perfectly find and also includes Tomas who saw fit to rail against it
yesterday regarding its compile times which can both tweaked and are also
worse still in some other popular languages) but I digress ...
Hope this helps, Dirk
ccode <- r"(
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;
)"
cprod <- inline::cfunction(sig=signature(mat1="numeric", mat2="numeric"),
body=ccode, language="C")
Rcpp::cppFunction("arma::mat cppprod(const arma::mat& m1, const arma::mat& m2)
{ return m1 * m2; }", depends="RcppArmadillo")
set.seed(123)
m1 <- matrix(rnorm(30), nrow = 60)
m2 <- matrix(rnorm(30), ncol = 60)
print(microbenchmark::microbenchmark(C = cprod(m1, m2),
R = m1 %*% m2,
Cpp = cppprod(m1, m2),
times = 100))
--
dirk.eddelbuettel.com | @eddelbuettel | e...@debian.org
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