12 thoughts on “OpenCV vs. Armadillo vs. Eigen on Linux”

1. You’ll get more speed out of Armadillo if you define ARMA_NO_DEBUG before including the Armadillo header (or -DARMA_NO_DEBUG on the command line). By default Armadillo has debugging enabled to aid correct algorithm development.

   Also, Armadillo uses Blas and Lapack for many routines, meaning you can use high speed Lapack replacement libraries like Intel MKL or AMD ACML. Instead of linking with the runtime component via -larmadillo (which is an alias to whatever Blas and Lapack libraries are installed), you can directly link with MKL and Armadillo will make use of it.

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   1. Thanks for the tip! I’ll have to do a second run with debugging turn off for all libraries, if possible. Something I overlooked. It’s useful to know Armadillo can support other linear algebra backend, but in the spirit of open source I’ll stick with BLAS/LAPACK for now 🙂

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      1. @nghiaho12
         Did you installed Blas and Lapack for Armadillo?
         I have some problems using Armadillo with Lapack and Blas.
         Can you post an article on how to make make them work together?
         Initially I installed BLAS, Lapack, and ATLAS but the matrix multiplication didn’t work, it gave me this errors:

         /usr/local/include/armadillo_bits/blas_wrapper.hpp|79|undefined reference to `wrapper_dgemv_’|

         /usr/local/include/armadillo_bits/blas_wrapper.hpp|114|undefined reference to `wrapper_dgemm_’|

         And now after I installed ACML I have the same problem.
         This is my C++ code:
         #include
         #include “armadillo”
         using namespace arma;
         using namespace std;
         int main()
         {
         mat A = “1 2;3 4;5 6”;
         mat B = “2 4 8;2 4 8”;
         mat C= A*B;
         cout<<C<<"\n";
         return 0;
         }

         Did you faced the same problem? Can you give me some help with this?

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         1. I used the default blas/lapack/atlas package that came with Ubuntu 11.x. I think the trick is to make sure you got up to date packages, relative to the Armadillo version you’re installing.

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         2. I had the same problem with wrapper_dgemm_ not defined. It went away when I added -larmadillo to the command line.

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2. No way!

   Original BLAS is very slow, try GOTOBlas http://www.tacc.utexas.edu/tacc-projects/gotoblas2 or ACML at least

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3. Eigen has its own vectorization system, so in the case that you are on a x86 system you would like to enable at least SSE2 by using -msse2

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   1. I compiled with gcc usnig -march=native, which according to GCC does:

      -mtune=native and -march=native will produce code optimized for the host architecture as detected using the cpuid instruction.

      So it should have all the SSE supported by my Intel i7 CPU.

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4. Pingback: Eigen: 一个线性代数的C++库 | bfcat-计算机视觉博客

5. Can you link against GPU matrix libraries such as BLAS when using armadillo?

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6. I meant *MAGMA

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   1. I’m not sure on this one. If they implement the BLAS routines faithfully and completely then it might be possible.

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