Performance: Matlab vs C++ Matrix vector multiplication - c++

Question

Preamble
Some time ago I asked a question about performance of Matlab vs Python (Performance: Matlab vs Python). I was surprised that Matlab is faster than Python, especially in meshgrid. In the discussion of that question, it was pointed to me that I should use a wrapper in Python to call my C++ code because C++ code is also available to me. I have the same code in C++, Matlab and Python.
While doing that, I was surprised once again to find that Matlab is faster than C++ in matrix assembly and computation.I have a slightly larger code, from which I am investigating a segment of matrix-vector multiplication. The larger code performs such multiplications at multiple instances. Overall the code in C++ is much much faster than Matlab (because function calling in Matlab has an overhead etc.), but Matlab seems to be outperforming C++ in the matrix-vector multiplication (code snippet at the bottom).
Results
The table below shows the comparison of time it takes to assemble the kernel matrix and the time it takes to multiply the matrix with the vector. The results are compiled for a matrix size NxN where N varies from 10,000 to 40,000. Which is not that large. But the interesting thing is that Matlab outperforms C++ the larger the N gets. Matlab is 3.8 - 5.8 times faster in total time. Moreover it is also faster in both matrix assembly and computation.
___________________________________________
|N=10,000 Assembly Computation Total |
|MATLAB 0.3387 0.031 0.3697 |
|C++ 1.15 0.24 1.4 |
|Times faster 3.8 |
___________________________________________
|N=20,000 Assembly Computation Total |
|MATLAB 1.089 0.0977 1.187 |
|C++ 5.1 1.03 6.13 |
|Times faster 5.2 |
___________________________________________
|N=40,000 Assembly Computation Total |
|MATLAB 4.31 0.348 4.655 |
|C++ 23.25 3.91 27.16 |
|Times faster 5.8 |
-------------------------------------------
Question
Is there a faster way of doing this in C++? Am I missing something? I understand that C++ is using for loops but my understanding is that Matlab will also be doing something similar in meshgrid.
Code Snippets
Matlab Code:
%% GET INPUT DATA FROM DATA FILES ------------------------------------------- %
% Read data from input file
Data = load('Input/input.txt');
location = Data(:,1:2);
charges = Data(:,3:end);
N = length(location);
m = size(charges,2);
%% EXACT MATRIX VECTOR PRODUCT ---------------------------------------------- %
kex1=ex1;
tic
Q = kex1.kernel_2D(location , location);
fprintf('\n Assembly time: %f ', toc);
tic
potential_exact = Q * charges;
fprintf('\n Computation time: %f \n', toc);
Class (Using meshgrid):
classdef ex1
methods
function [kernel] = kernel_2D(obj, x,y)
[i1,j1] = meshgrid(y(:,1),x(:,1));
[i2,j2] = meshgrid(y(:,2),x(:,2));
kernel = sqrt( (i1 - j1) .^ 2 + (i2 - j2) .^2 );
end
end
end
C++ Code:
EDIT
Compiled using a make file with following flags:
CC=g++
CFLAGS=-c -fopenmp -w -Wall -DNDEBUG -O3 -march=native -ffast-math -ffinite-math-only -I header/ -I /usr/include
LDFLAGS= -g -fopenmp
LIB_PATH=
SOURCESTEXT= src/read_Location_Charges.cpp
SOURCESF=examples/matvec.cpp
OBJECTSF= $(SOURCESF:.cpp=.o) $(SOURCESTEXT:.cpp=.o)
EXECUTABLEF=./exec/mykernel
mykernel: $(SOURCESF) $(SOURCESTEXT) $(EXECUTABLEF)
$(EXECUTABLEF): $(OBJECTSF)
$(CC) $(LDFLAGS) $(KERNEL) $(INDEX) $(OBJECTSF) -o $# $(LIB_PATH)
.cpp.o:
$(CC) $(CFLAGS) $(KERNEL) $(INDEX) $< -o $#
`
# include"environment.hpp"
using namespace std;
using namespace Eigen;
class ex1
{
public:
void kernel_2D(const unsigned long M, double*& x, const unsigned long N, double*& y, MatrixXd& kernel) {
kernel = MatrixXd::Zero(M,N);
for(unsigned long i=0;i<M;++i) {
for(unsigned long j=0;j<N;++j) {
double X = (x[0*N+i] - y[0*N+j]) ;
double Y = (x[1*N+i] - y[1*N+j]) ;
kernel(i,j) = sqrt((X*X) + (Y*Y));
}
}
}
};
int main()
{
/* Input ----------------------------------------------------------------------------- */
unsigned long N = 40000; unsigned m=1;
double* charges; double* location;
charges = new double[N * m](); location = new double[N * 2]();
clock_t start; clock_t end;
double exactAssemblyTime; double exactComputationTime;
read_Location_Charges ("input/test_input.txt", N, location, m, charges);
MatrixXd charges_ = Map<MatrixXd>(charges, N, m);
MatrixXd Q;
ex1 Kex1;
/* Process ------------------------------------------------------------------------ */
// Matrix assembly
start = clock();
Kex1.kernel_2D(N, location, N, location, Q);
end = clock();
exactAssemblyTime = double(end-start)/double(CLOCKS_PER_SEC);
//Computation
start = clock();
MatrixXd QH = Q * charges_;
end = clock();
exactComputationTime = double(end-start)/double(CLOCKS_PER_SEC);
cout << endl << "Assembly time: " << exactAssemblyTime << endl;
cout << endl << "Computation time: " << exactComputationTime << endl;
// Clean up
delete []charges;
delete []location;
return 0;
}

Answer 1

As said in the comments MatLab relies on Intel's MKL library for matrix products, which is the fastest library for such kind of operations. Nonetheless, Eigen alone should be able to deliver similar performance. To this end, make sure to use latest Eigen (e.g. 3.4), and proper compilation flags to enable AVX/FMA if available and multithreading:
-O3 -DNDEBUG -march=native
Since charges_ is a vector, better use a VectorXd to Eigen knows that you want a matrix-vector product and not a matrix-matrix one.
If you have Intel's MKL, then you can also let Eigen uses it to get exact same performance than MatLab for this precise operation.
Regarding the assembly, better inverse the two loops to enable vectorization, then enable multithreading with OpenMP (add -fopenmp as compiler flags) to make the outermost loop run in parallel, and finally you can simplify your code using Eigen:
void kernel_2D(const unsigned long M, double* x, const unsigned long N, double* y, MatrixXd& kernel) {
kernel.resize(M,N);
auto x0 = ArrayXd::Map(x,M);
auto x1 = ArrayXd::Map(x+M,M);
auto y0 = ArrayXd::Map(y,N);
auto y1 = ArrayXd::Map(y+N,N);
#pragma omp parallel for
for(unsigned long j=0;j<N;++j)
kernel.col(j) = sqrt((x0-y0(j)).abs2() + (x1-y1(j)).abs2());
}
With multi-threading you need to measure the wall clock time. Here (Haswell with 4 physical cores running at 2.6GHz) the assembly time drops to 0.36s for N=20000, and the matrix-vector products take 0.24s so 0.6s in total that is faster than MatLab whereas my CPU seems to be slower than yours.

Answer 2

You might be interested to look at the MATLAB Central contribution mtimesx.
Mtimesx is a mex function that optimizes matrix multiplications using the BLAS library, openMP and other methods. In my experience, when it was originally posted it could be beat stock MATLAB by 3 orders of magnitude in some cases. (Somewhat embarrassing for MATHWORKS, I presume.) These days MATLAB has improved its own methods (I suspect borrowing from this.) and the differences are less severe. MATLAB sometimes out-performs it.