dot product profiling, see bottom of file for execution speeds

eigen profile.cpp

#include "include/eigen/dense"

int main() {

  const unsigned max = 86800;
//	long double v[max][2];
//	long double d[2] = {3.14159e5, 82.31283e3};
	Eigen::Matrix<long double, 2, 1> v[max];
	Eigen::Matrix<long double, 2, 1> d;
	d(0,0) = 3.14159e5;
	d(1,0) = 82.31283e3;

	unsigned n, i;
	n=0;
	while (n != max){
		v[n](0,0) = 3452.2;
		v[n++](1,0) = 3.1e2;
	}
	long double e;
	for(i=0; i<100; ++i) {
		n=0;
		while (n != max) {
			e = v[++n].dot(d);
		}
	}

	return 0;
}

//this completed in ~4.045s

matrix profile.cpp

//#include "include/eigen/dense"

int main() {

  const unsigned max = 86800;
  long double v[max][2];
	long double d[2] = {3.14159e5, 82.31283e3};
//	Eigen::Matrix<long double, 2, 1> v[max];
//	Eigen::Matrix<long double, 2, 1> d;
//	d(0,0) = 3.14159e5;
//	d(1,0) = 82.31283e3;

	unsigned n, i;
	n=0;
	while (n != max){//initialize array of 2D arrays
		v[n][0] = 3452.2;
		v[n++][1] = 3.1e2;
	}
	long double e;   //scalar to assign dot product to
	for(i=0; i<100; ++i) {//find dot product of vector 'n' with vector 'd', 100x
		n=0;
		while (n != max) {
			e = v[n][0] * d[0] + v[n][1] * d[1];
			++n;
		}
	}

	return 0;
}

//this completed in ~0.110s