Rcpp rowMaxs vs. matrixStats rowMaxs - r

Question

I am trying to efficiently compute rowMaxs in Rcpp. A very simple implementation is
arma::mat RcppRowmaxs(arma::mat x){
int N = x.n_rows;
arma::mat rm(N,1);
for(int nn = 0; nn < N; nn++){
rm(nn) = max(x.row(nn));
}
return(rm);
}
which works perfectly fine. However, comparing this function to other packages, it turned out that other implementations are by far more efficient. Specifically, Rfast::rowMaxs is more than 6 times faster than the simple Rcpp implementation!
Naturally, I tried to mimic the behavior of Rfast.
However, as a beginner in Rcpp, I only tried to load Rfast::rowMaxs directly in Rcpp as described e.g. here. Unfortunately, using an Rcpp script to load an R function that again calls an Rcpp script seems pretty slow following my benchmark (see row "RfastinRcpp"):
m = matrix(rnorm(1000*1000),1000,1000)
microbenchmark::microbenchmark(
matrixStats = matrixStats::rowMaxs(m),
Rfast = Rfast::rowMaxs(m,value=T),
Rcpp = RcppRowmaxs(m),
RfastinRcpp = RfastRcpp(m),
apply = apply(m,1,max)
)
Unit: microseconds
expr min lq mean median uq max neval cld
matrixStats 1929.570 2042.8975 2232.1980 2086.5180 2175.470 4025.923 100 a
Rfast 666.711 727.2245 842.5578 795.2215 891.443 1477.969 100 a
Rcpp 5552.216 5825.4855 6186.9850 5997.8295 6373.737 8568.878 100 b
RfastinRcpp 7495.042 7931.2480 9471.8453 8382.6350 10659.672 19968.817 100 b
apply 12281.758 15145.7495 22015.2798 17202.9730 20310.939 136844.591 100 c
Any tips on how to improve performance in the function I've provided above? I've looked at the source code from Rfast and believe that this is the correct file. However, so far I did not manage to locate the important parts of the code.
Edit: Changed the post to focus on Rfast now, following the answer of Michail.

Answer 1

I just did some experiments on my laptop. I have an 5 year old HP with 2 intel i5 cores at 2.3 GHz. Attached is an picture with my results. Rfast's implementation is way faster than matrixStats' implementation, always and as the matrix gets bigger the time difference increases.

Answer 2

library(Rfast)
library(microbenchmark)
library(matrixStats)
x <- matrnorm(100,100)
microbenchmark(Rfast::rowMaxs(x,value=TRUE), matrixStats::rowMaxs(x),times=10)
Unit: microseconds
expr min lq mean median uq max neval
Rfast::rowMaxs(x, value = TRUE) 20.5 20.9 242.64 21.50 23.2 2223.8 10
matrixStats::rowMaxs(x) 43.7 44.7 327.43 46.95 88.2 2776.8 10
x <- matrnorm(1000,1000)
microbenchmark(Rfast::rowMaxs(x,value=TRUE), matrixStats::rowMaxs(x),times=10)
Unit: microseconds
expr min lq mean median uq max neval
Rfast::rowMaxs(x, value = TRUE) 799.5 844.0 875.08 858.5 900.3 960.3 10
matrixStats::rowMaxs(x) 2229.8 2260.8 2303.04 2269.4 2293.3 2607.8 10
x <- matrnorm(10000,10000)
microbenchmark(Rfast::rowMaxs(x,value=TRUE), matrixStats::rowMaxs(x),times=10)
Unit: milliseconds
expr min lq mean median uq max neval
Rfast::rowMaxs(x, value = TRUE) 82.1157 83.4288 85.81769 84.57885 86.2742 93.0031 10
matrixStats::rowMaxs(x) 216.0003 218.5324 222.46670 221.00330 225.3302 237.7666 10