CODEWITHSUNDEEP
pythonnumpyscipy
function_store
function_store

Reputation: 373

Prebuilt numpy with BLAS/ATLAS?

I'm implementing a real-time LMS algorithm, and numpy.dot takes more time than my sampling time, so I need numpy to be faster (my matrices are 1D and 100 long).

I've read about building numpy with ATLAS and such, but never done such thing and spent all my day trying to do it, with zero succes...

Can someone explain why there aren't builds with ATLAS included? Can anyone provide me with one? Is there any other way to speed up dot product?

I've tried numba, and scipy.linalg.gemm_dot but none of them seemed to speed things up.

my system is Windows8.1 with Intel processor

Upvotes: 1

Views: 1291

Answers (1)

Davidmh
Davidmh

Reputation: 3865

If you download the official binaries, they should come linked with ATLAS. If you want to make sure, check the output of np.show_config(). The problem is that ATLAS (Automatically Tuned Linear Algebra System) checks many different combinations and algorithms, and keeps the best at compile time. So, when you run a precompiled ATLAS, you are running it optimised for a computer different than yours.

So, your options to improve dot are:

  • Compile ATLAS yourself. On Windows it may be a bit challenging, but it is doable. Note: you must use the same compiler used to compile Python. That is, if you decide to go for MinGW, you need to get Python compiled with MinGW, or build it yourself.
  • Try Christopher Gohlke's Numpy. It is linked against MKL, that is much faster than ATLAS (and does all the optimisations at run time).
  • Try Continuum analytics' Conda with accelerate (also linked with MKL). It costs money, unless you are an academic. In Linux, Conda is slower than system python because they have to use an old compiler for compatibility purposes; I don't know if that is the case on Windows.
  • Use Linux. Your Python life will be much easier, setting up the system to compile stuff is very easy. Also, setting up Cython is simple too, and then you can compile your whole algorithm, and probably get further speed up.

The note regarding Cython is valid for Windows too, it is just more difficult to get it working. I tried a few years ago (when I used Windows), and failed after a few days; I don't know if the situation has improved.

Alternative:

You are doing the dot product of two vectors. Then, np.dot is probably not the most efficient way. I would give a shot to spell it out in plain Python (vec1*vec2).sum() (could be very good for Numba, this expression it can actually optimise) or using numexpr:

ne.evaluate(`sum(vec1 * vec2)`)

Numexpr will also parallelise the expression automatically.

Upvotes: 4

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