Benchmarking (python vs. c++ using BLAS) and (numpy)

Question

I would like to write a program that makes extensive use of BLAS and LAPACK linear algebra functionalities. Since performance is an issue I did some benchmarking and would l

Answer 1

Here's another benchmark (on Linux, just type make): http://dl.dropbox.com/u/5453551/blas_call_benchmark.zip

http://dl.dropbox.com/u/5453551/blas_call_benchmark.png

I do not see essentially any difference between the different methods for large matrices, between Numpy, Ctypes and Fortran. (Fortran instead of C++ --- and if this matters, your benchmark is probably broken.)

Your CalcTime function in C++ seems to have a sign error. ... + ((double)start.tv_usec)) should be instead ... - ((double)start.tv_usec)). Perhaps your benchmark also has other bugs, e.g., comparing between different BLAS libraries, or different BLAS settings such as number of threads, or between real time and CPU time?

EDIT: failed to count the braces in the CalcTime function -- it's OK.

As a guideline: if you do a benchmark, please always post all the code somewhere. Commenting on benchmarks, especially when surprising, without having the full code is usually not productive.

---

To find out which BLAS Numpy is linked against, do:

$ python
Python 2.7.2+ (default, Aug 16 2011, 07:24:41) 
[GCC 4.6.1] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy.core._dotblas
>>> numpy.core._dotblas.__file__
'/usr/lib/pymodules/python2.7/numpy/core/_dotblas.so'
>>> 
$ ldd /usr/lib/pymodules/python2.7/numpy/core/_dotblas.so
    linux-vdso.so.1 =>  (0x00007fff5ebff000)
    libblas.so.3gf => /usr/lib/libblas.so.3gf (0x00007fbe618b3000)
    libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007fbe61514000)

UPDATE: If you can't import numpy.core._dotblas, your Numpy is using its internal fallback copy of BLAS, which is slower, and not meant to be used in performance computing! The reply from @Woltan below indicates that this is the explanation for the difference he/she sees in Numpy vs. Ctypes+BLAS.

To fix the situation, you need either ATLAS or MKL --- check these instructions: http://scipy.org/Installing_SciPy/Linux Most Linux distributions ship with ATLAS, so the best option is to install their libatlas-dev package (name may vary).