How to implement an array of 3d vectors

Question

I decided to use Eigen library in my project. But it is not clear from documentation how the most efficiently one should specify an array of 3d vectors.

As I suggest, the first way is

Eigen::Matrix<Eigen::Vector3d, Eigen::Dynamic, 1> array_of_v3d(size);  

But in that case how should I get another array which elements are equal to scalar products of elements of array_of_v3d and some other instance of Vector3d? In other words, can I rewrite the following loop using Eigen's functions:

Eigen::Vector3d v3d(0.5, 0.5, 0.5);  
Eigen::VectorXd other_array(size);  
for (size_t i = 0; i < size; ++i)
    other_array(i) = array_of_v3d(i).dot(v3d);

The second way is to use a matrix which size is (3 x size) or (size x 3). For example, I can declare it like this:

Eigen::Matrix<double, 3, Eigen::Dynamic> matrix;  

But I didn't get from the documentation how to set the number of columns. The following seems to work but I have to repeat the number of rows 3 twice:

Eigen::Matrix<double, 3, Eigen::Dynamic> matrix(3, size);  

Then the above loop is equivalent to

other_array = v3d.transpose() * array_of_v3d;

As my experiments shown that this is a little bit faster than

Eigen::Matrix<double, Eigen::Dynamic, 3> matrix(size, 3);  
other_array = array_of_v3d * v3d;

In addition:

Anyway, my use of Eigen seems to be not so optimal since the same program in plain C is almost 1.5 times faster (it is not true indeed, it depends on size):

for (size_t i = 0; i < size; i+=4) {
    s[i]   += a[i]   * x + b[i]   * y  + c[i]   * z;
    s[i+1] += a[i+1] * x + b[i+1] * y  + c[i+1] * z;
    s[i+2] += a[i+2] * x + b[i+2] * y  + c[i+2] * z;
    s[i+3] += a[i+3] * x + b[i+3] * y  + c[i+3] * z;
}

Have I missed something? Is there some other way in the scope of Eigen library to solve my problem?

Update:

Here I present results of my testings. There are 5 cases:

1. C-style for loop which is partially unrolled
2. Eigen::Matrix (rows x cols = 3 x size). In this case values of 3d vectors are stored together in memory because by default Eigen stores data in column-major. Or I could set Eigen::RowMajor and everything else like in next case.
3. Eigen::Matrix (rows x cols = size x 3).
4. Here each component of 3d vector is stored on a separate VectorXd. So there are three VectorXd objects which are put together on Vector3d.
5. An std::vector container is used to store Vector3d objects.

This is my test program

#include <iostream>
#include <iomanip>
#include <vector>
#include <ctime>

#include <Eigen/Dense>

double for_loop(size_t rep, size_t size)
{
    std::vector<double>  a(size), b(size), c(size);
    double x = 1, y = 2, z = - 3;
    std::vector<double>  s(size);
    for(size_t i = 0; i < size; ++i) {
        a[i] = i;
        b[i] = i;
        c[i] = i;
        s[i] = 0;
    }
    double dtime = clock();
    for(size_t j = 0; j < rep; j++) 
        for(size_t i = 0; i < size; i += 8) {
            s[i] += a[i]   * x + b[i]   * y  + c[i]   * z;
            s[i] += a[i+1] * x + b[i+1] * y  + c[i+1] * z;
            s[i] += a[i+2] * x + b[i+2] * y  + c[i+2] * z;
            s[i] += a[i+3] * x + b[i+3] * y  + c[i+3] * z;
            s[i] += a[i+4] * x + b[i+4] * y  + c[i+4] * z;
            s[i] += a[i+5] * x + b[i+5] * y  + c[i+5] * z;
            s[i] += a[i+6] * x + b[i+6] * y  + c[i+6] * z;
            s[i] += a[i+7] * x + b[i+7] * y  + c[i+7] * z;
        }
    dtime = (clock() - dtime) / CLOCKS_PER_SEC;

    double res = 0;
    for(size_t i = 0; i < size; ++i)
        res += std::abs(s[i]);
    assert(res == 0.);

    return dtime;
}

double eigen_3_size(size_t rep, size_t size)
{
    Eigen::Matrix<double, 3, Eigen::Dynamic> A(3, size);
    Eigen::Matrix<double, 1, Eigen::Dynamic> S(size);
    Eigen::Vector3d X(1, 2, -3);
    for(size_t i = 0; i < size; ++i) {
        A(0, i) = i;
        A(1, i) = i;
        A(2, i) = i;
        S(i)    = 0;
    }
    double dtime = clock();
    for (size_t j = 0; j < rep; j++) 
        S.noalias() += X.transpose() * A;
    dtime = (clock() - dtime) / CLOCKS_PER_SEC;

    double res = S.array().abs().sum();
    assert(res == 0.);

    return dtime;
}

double eigen_size_3(size_t rep, size_t size)
{
    Eigen::Matrix<double, Eigen::Dynamic, 3> A(size, 3);
    Eigen::Matrix<double, Eigen::Dynamic, 1> S(size);
    Eigen::Vector3d X(1, 2, -3);
    for(size_t i = 0; i < size; ++i) {
        A(i, 0) = i;
        A(i, 1) = i;
        A(i, 2) = i;
        S(i)    = 0;
    }
    double dtime = clock();
    for (size_t j = 0; j < rep; j++) 
        S.noalias() += A * X;
    dtime = (clock() - dtime) / CLOCKS_PER_SEC;

    double res = S.array().abs().sum();
    assert(res == 0.);

    return dtime;
}

double eigen_vector3_vector(size_t rep, size_t size)
{
    Eigen::Matrix<Eigen::VectorXd, 3, 1> A;
    A(0).resize(size);
    A(1).resize(size);
    A(2).resize(size);
    Eigen::VectorXd S(size);
    Eigen::Vector3d X(1, 2, -3);
    for(size_t i = 0; i < size; ++i) {
        A(0)(i) = i;
        A(1)(i) = i;
        A(2)(i) = i;
        S(i)    = 0;
    }
    double dtime = clock();
    for (size_t j = 0; j < rep; j++) 
        S.noalias() += A(0) * X(0) + A(1) * X(1) + A(2) * X(2);
    dtime = (clock() - dtime) / CLOCKS_PER_SEC;

    double res = S.array().abs().sum();
    assert(res == 0.);

    return dtime;
}

double eigen_stlvector_vector3(size_t rep, size_t size)
{
    std::vector<                 Eigen::Vector3d,
        Eigen::aligned_allocator<Eigen::Vector3d> > A(size);
    std::vector<double> S(size);
    Eigen::Vector3d X(1, 2, -3);
    for(size_t i = 0; i < size; ++i) {
        A[i](0) = i;
        A[i](1) = i;
        A[i](2) = i;
        S[i]    = 0;
    }
    double dtime = clock();
    for (size_t j = 0; j < rep; j++) 
        for(size_t i = 0; i < size; i++) 
            S[i] += X.dot(A[i]);
    dtime = (clock() - dtime) / CLOCKS_PER_SEC;

    double res = 0;
    for(size_t i = 0; i < size; i++) 
        res += std::abs(S[i]);
    assert(res == 0.);

    return dtime;
}

int main()
{
    std::cout << "    size    |  for loop  |   Matrix   |   Matrix   | Vector3 of  | STL vector of \n" 
              << "            |            |  3 x size  |  size x 3  | Vector_size |  TinyVectors  \n" 
              << std::endl;

    size_t n       = 10;
    size_t sizes[] = {16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192};
    int rep_all    = 1024 * 1024 * 1024;

    for (int i = 0; i < n; ++i) {
        size_t size = sizes[i];
        size_t rep = rep_all / size;

        double t1 = for_loop                (rep, size);
        double t2 = eigen_3_size            (rep, size);
        double t3 = eigen_size_3            (rep, size);
        double t4 = eigen_vector3_vector    (rep, size);
        double t5 = eigen_stlvector_vector3 (rep, size);

        using namespace std;
        cout << setw(8)  << size 
             << setw(13) << t1 << setw(13) << t2 << setw(13) << t3
             << setw(14) << t4 << setw(15) << t5 << endl;
    }
}

The program was compiled by gcc 4.6 with options -march=native -O2 -msse2 -mfpmath=sse. On my Athlon 64 X2 4600+ I got a nice table:

  size    |  for loop  |   Matrix   |   Matrix   | Vector3 of  | STL vector of 
          |            |  3 x size  |  size x 3  | Vector_size |  TinyVectors  

    16         2.23          3.1         3.29          1.95           3.34
    32         2.12         2.72         3.51          2.25           2.95
    64         2.15         2.52         3.27          2.03           2.74
   128         2.22         2.43         3.14          1.92           2.66
   256         2.19         2.38         3.34          2.15           2.61
   512         2.17         2.36         3.54          2.28           2.59
  1024         2.16         2.35         3.52          2.28           2.58
  2048         2.16         2.36         3.43          2.42           2.59
  4096        11.57         5.35        20.29         13.88           5.23
  8192        11.55         5.31        16.17         13.79           5.24

The table shows that good representations of an array of 3d vectors are Matrix (components of 3d vectors should stored together) and std::vector of fixed size Vector3d objects. This confirms Jakob's answer. For big vectors Eigen indeed shows good results.

Zampardi

Can post a simple, complete test program that demonstrates the speed difference between Eigen and C style arrays? – Stavro

Answer 1

If you just want to hold an array of Vector3d objects, usually using std::vector is fine, although you need to be aware of the alignment issues.

The second method you describe which uses a size x 3 matrix also is very workable and usually the faster way. I am not sure if you are asking a question on this one, other than the performance question.

As to the performance, I assume that you did use full optimization in your compiler, as Eigen does not perform well, when optimization is switched off. In any case, you may be able to gain some performance by using aligned types which can processed using optimised SIMD instructions. Eigen should do this automatically if you used e.g. Vector4d instead.

Dinkins

Thank you, Jakob. The methods you've proposed indeed seem to be the best ones. – Zampardi

the link for aligned types is broken as of 2016.03.22 – Patrilocal

Fixed the broken links. – Dinkins

Answer 2

Running the same benchmark in 2021, using MSVC 16 2019 (cl version 19.28.29913), 64 bit, in Release mode, with Eigen 3.3.9, on an Intel Core i7-10750H:

    size    |  for loop  |   Matrix   |   Matrix   | Vector3 of  | STL vector of
            |            |  3 x size  |  size x 3  | Vector_size |  TinyVectors

      16        0.747        19.49        1.013         0.967          0.909
      32        1.113       19.536        0.942         0.876          0.909
      64        0.728       19.487        1.118         0.962          1.001
     128        0.721       19.546        0.979         0.864          0.928
     256        0.878       19.428        1.004         0.936          0.937
     512        0.922       19.496         1.02         0.985          0.917
    1024        0.937       19.434        1.044         1.004          0.919
    2048        0.969       19.479        1.104         1.074          0.977
    4096         0.97       19.531        1.108         1.074          0.987
    8192        1.031       19.596        1.194         1.164          1.025

Jasminejason