Hi,
another option if you're using Linux AND an Intel processor would be
linking R against Intel MKL (Math Kernel Library). Under Linux you can
get a (free) non-commercial licence for it.
Here I'm using an Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz laptop
processor with R 3.0.2 build with intel compilers and linked against
Intel MKL 11 and get the following times:
set.seed(123)
n <- 2000
A<-matrix(rnorm(n^2,0,1), n,n)
system.time(D<-A%*%A%*%A+A)
User System verstrichen
1.480 0.004 1.482
PS: I'm using the sequential version of Intel MKL.
Zitat von Timo Schmid <timo_sch...@hotmail.com>:
Hello,
I am looking for a way to do fast matrix operations (multiplication,
Inversion) for
large matrices (n=8000) in R. I know R is not that fast in linear
algebra than
other software.
So I wanted to write some code in C++ and incorporate this code in
R. I have used the
package RcppArmadillo, because a lot of people write that it is
really fast in
doing matrix algebra. So I have run a short example. See the code below.
I was wondering that I got almost the same CPU time for the matrix
algebra in my
example. I expect that using C++ Code in R is faster than using the standard
matrix operations in R.
Is there a way to do matrix algebra in R faster as the standard
command (e.g. %*%) using
the Rcpp or RcppArmadillo packages? I would be happy about any idea
or advice.
Thanks in advance
> library(Rcpp)
library(RcppArmadillo)
library(inline)
library(RcppEigen)
library(devtools)
# Generation of the matrix
n=2000
A<-matrix(rnorm(n^2,0,1), n,n)
# Code in R
system.time(
+ D<-A%*%A%*%A+A)
user system elapsed
12.29 0.01 12.33
# Code using RcppArmadillo
src <-
+ '
+ arma::mat X = Rcpp::as<arma::mat>(X_);
+ arma::mat ans = X * X * X + X;
+ return(wrap(ans));
+ '
mprod6_inline_RcppArma <- cxxfunction(signature(X_="numeric"),
+ body = src, plugin="RcppArmadillo")
system.time(
+ C<-mprod6_inline_RcppArma(X=A))
user system elapsed
12.30 0.08 12.40
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