C++ is way slower than MATLAB - c++

Question

I am trying to generate 5000 by 5000 random number matrix. Here is what I do with MATLAB:
for i = 1:100
rand(5000)
end
And here is what I do in C++:
#include <iostream>
#include <stdlib.h>
#include <time.h>
#include <ctime>
using namespace std;
int main(){
int N = 5000;
double ** A = new double*[N];
for (int i=0;i<N;i++)
A[i] = new double[N];
srand(time(NULL));
clock_t start = clock();
for (int k=0;k<100;k++){
for (int i=0;i<N;i++){
for (int j=0;j<N;j++){
A[i][j] = rand();
}
}
}
cout << "T="<< (clock()-start)/(double)(CLOCKS_PER_SEC/1000)<< "ms " << endl;
}
MATLAB takes around 38 seconds while C++ takes around 90 seconds.
In another question, people executed the same code and got same speeds for both C++ and MATLAB.
I am using visual C++ with the following optimizations
I would like to learn what I am missing here? Thank you for all the help.
EDIT: Here is the key thing though...
Why MATLAB is faster than C++ in creating random numbers?
In this question, people gave me answers where their C++ speeds are same as MATLAB. When I use the same code I get way worse speeds and I am trying to understand why.

Answer 1

Your test is flawed, as others have noted, and does not even address the statement made by the title. You are comparing an inbuilt Matlab function to C++, not Matlab code itself, which in fact executes 100x more slowly than C++. Matlab is just a wrapper around the BLAS/LAPACK libraries in C/Fortran so one would expect a Matlab script, and a competently written C++ to be approximately equivalent, and indeed they are: This code in Matlab 2007b
tic; A = rand(5000); toc
executes in 810ms on my machine and this
#include <iostream>
#include <stdlib.h>
#include <time.h>
#include <ctime>
#define N 5000
int main()
{
srand(time(NULL));
clock_t start = clock();
int num_rows = N,
num_cols = N;
double * A = new double[N*N];
for (int i=0; i<N*N; ++i)
A[i] = rand();
std::cout << "T="<< (clock()-start)/(double)(CLOCKS_PER_SEC/1000)<< "ms " << std::endl;
return 0;
}
executes in 830ms. A slight advantage for Matlab's in-house RNG over rand() is not too surprising. Note also the single indexing. This is how Matlab does it, internally. It then uses a clever indexing system (developed by others) to give you a matrix-like interface to the data.

Answer 2

In your C++ code, you are doing 5000 allocations of double[5000] on the heap. You would probably get much better speed if you did a single allocation of a double[25000000], and then do your own arithmetic to convert your 2 indices to a single one.

Answer 3

I believe MATLAB utilize multiple cpu cores on your machine. Have you try to write a multi-threaded version and measure the difference?
Also, the quality of (pseudo) random would also make slightly difference (but not that much).

Answer 4

In my experience,
First check that you execute your C++ code in release mode instead of in Debug mode. (Although I see in the picture you are in release mode)
Consider MPI parallelization.
Bear in mind that MATLAB is highly optimized and compiled with the Intel compiler which produces faster executables. You can also try more advanced compilers if you can afford them.
Last you can make a loop aggregation by using a function to generate combinations of i, j in a single loop. (In python this is a common practice given by the function product from the itertools library, see this)
I hope it helps.