Eigen vs Matlab: parallelized Matrix-Multiplication

I would like to compare the speed of Matlab in matrix multiplication with the speed of Eigen 3 on an Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz. The code including Eigen:

#include <iostream>
#include "Eigen/Dense"
#include <chrono>
#include <omp.h>


using namespace std;
using namespace Eigen;

const int dim=100;

int main()
{
    std::chrono::time_point<std::chrono::system_clock> start, end;

    int n;
    n = Eigen::nbThreads();
    cout<<n<<"\n";

    Matrix<double, Dynamic, Dynamic> m1(dim,dim);
    Matrix<double, Dynamic, Dynamic> m2(dim,dim);
    Matrix<double, Dynamic, Dynamic> m_res(dim,dim);

    start = std::chrono::system_clock::now();

    for (int i = 0 ; i <100000; ++i) {
        m1.setRandom(dim,dim);
        m2.setRandom(dim,dim);
        m_res=m1*m2;

    }

    end = std::chrono::system_clock::now();
    std::chrono::duration<double> elapsed_seconds = end-start;

    std::cout << "elapsed time: " << elapsed_seconds.count() << "s\n";

    return 0;
}

It is compiled with g++ -O3 -std=c++11 -fopenmp and executed with OMP_NUM_THREADS=8 ./prog. In Matlab I'm using

function mat_test(N,dim)
%
% N:    how many tests
% dim:  dimension of the matrices

tic
parfor i=1:N
     A = rand(dim);
     B = rand(dim);
     C = A*B;
end
toc

The result is: 9s for Matlab, 36s for Eigen. What am I doing wrong in the Eigen case? I can exclude the dynamic allocation of of the matrices. Also, only 3 threads are used instead of eight.