performance - Why is this naive matrix multiplication faster than base R's?

Question

In R, matrix multiplication is very optimized, i.e. is really just a call to BLAS/LAPACK. However, I'm surprised this very naive C++ code for matrix-vector multiplication seems reliably 30% faster.

 library(Rcpp)

 # Simple C++ code for matrix multiplication
 mm_code = 
 "NumericVector my_mm(NumericMatrix m, NumericVector v){
   int nRow = m.rows();
   int nCol = m.cols();
   NumericVector ans(nRow);
   double v_j;
   for(int j = 0; j < nCol; j++){
     v_j = v[j];
     for(int i = 0; i < nRow; i++){
       ans[i] += m(i,j) * v_j;
     }
   }
   return(ans);
 }
 "
 # Compiling
 my_mm = cppFunction(code = mm_code)

 # Simulating data to use
 nRow = 10^4
 nCol = 10^4

 m = matrix(rnorm(nRow * nCol), nrow = nRow)
 v = rnorm(nCol)

 system.time(my_ans <- my_mm(m, v))
#>    user  system elapsed 
#>   0.103   0.001   0.103 
 system.time(r_ans <- m %*% v)
#>   user  system elapsed 
#>  0.154   0.001   0.154 

 # Double checking answer is correct
 max(abs(my_ans - r_ans))
 #> [1] 0

Does base R's %*% perform some type of data check that I'm skipping over?

EDIT:

After understanding what's going on (thanks SO!), it's worth noting that this is a worst case scenario for R's %*%, i.e. matrix by vector. For example, @RalfStubner pointed out that using an RcppArmadillo implementation of a matrix-vector multiply is even faster than the naive implementation that I demonstrated, implying considerable faster than base R, but is virtually identical to base R's %*% for matrix-matrix multiply (when both matrices are large and square):

 arma_code <- 
   "arma::mat arma_mm(const arma::mat& m, const arma::mat& m2) {
 return m * m2;
 };"
 arma_mm = cppFunction(code = arma_code, depends = "RcppArmadillo")

 nRow = 10^3 
 nCol = 10^3

 mat1 = matrix(rnorm(nRow * nCol), 
               nrow = nRow)
 mat2 = matrix(rnorm(nRow * nCol), 
               nrow = nRow)

 system.time(arma_mm(mat1, mat2))
#>   user  system elapsed 
#>   0.798   0.008   0.814 
 system.time(mat1 %*% mat2)
#>   user  system elapsed 
#>   0.807   0.005   0.822  

So R's current (v3.5.0) %*% is near optimal for matrix-matrix, but could be significantly sped up for matrix-vector if you're okay skipping the checking.

Answer 1

A quick glance in names.c (here in particular) points you to do_matprod, the C function that is called by %*% and which is found in the file array.c. (Interestingly, it turns out, that both crossprod and tcrossprod dispatch to that same function as well). Here is a link to the code of do_matprod.

Scrolling through the function, you can see that it takes care of a number of things your naive implementation does not, including:

1. Keeps row and column names, where that makes sense.
2. Allows for dispatch to alternative S4 methods when the two objects being operated on by a call to %*% are of classes for which such methods have been provided. (That's what's happening in this portion of the function.)
3. Handles both real and complex matrices.
4. Implements a series of rules for how to handle multiplication of a matrix and a matrix, a vector and a matrix, a matrix and a vector, and a vector and a vector. (Recall that under cross-multiplication in R, a vector on the LHS is treated as a row vector, whereas on the RHS, it is treated as a column vector; this is the code that makes that so.)

Near the end of the function, it dispatches to either of matprod or or cmatprod. Interestingly (to me at least), in the case of real matrices, if either matrix might contain NaN or Inf values, then matprod dispatches (here) to a function called simple_matprod which is about as simple and straightforward as your own. Otherwise, it dispatches to one of a couple of BLAS Fortran routines which, presumably are faster, if uniformly 'well-behaved' matrix elements can be guaranteed.