very fast OLS regression?

Dear R experts:

I just tried some simple test that told me that hand computing the OLS
coefficients is about 3-10 times as fast as using the built-in lm()
function.   (code included below.)  Most of the time, I do not care,
because I like the convenience, and I presume some of the time goes
into saving a lot of stuff that I may or may not need.  But when I do
want to learn the properties of an estimator whose input contains a
regression, I do care about speed.

What is the recommended fastest way to get regression coefficients in
R?  (Is Gentlemen's weighted-least-squares algorithm implemented in a
low-level C form somewhere?  that one was always lightning fast for
me.)

regards,

/ivo



bybuiltin = function( y, x )   coef(lm( y ~ x -1 ));

byhand = function( y, x ) {
  xy<-t(x)%*%y;
  xxi<- solve(t(x)%*%x)
  b<-as.vector(xxi%*%xy)
  ## I will need these later, too:
  ## res<-y-as.vector(x%*%b)
  ## soa[i]<-b[2]
  ## sigmas[i]<-sd(res)
  b;
}


MC=500;
N=10000;


set.seed(0);
x= matrix( rnorm(N*MC), nrow=N, ncol=MC );
y= matrix( rnorm(N*MC), nrow=N, ncol=MC );

ptm = proc.time()
for (mc in 1:MC) byhand(y[,mc],x[,mc]);
cat("By hand took ", proc.time()-ptm, "\n");

ptm = proc.time()
for (mc in 1:MC) bybuiltin(y[,mc],x[,mc]);
cat("By built-in took ", proc.time()-ptm, "\n");