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 like know, if the approach I took is legitimate.

I have, so to speak, three contestants and want to test their performance with a simple matrix-matrix multiplication. The contestants are:

1. Numpy, making use only of the functionality of dot.
2. Python, calling the BLAS functionalities through a shared object.
3. C++, calling the BLAS functionalities through a shared object.

Scenario

I implemented a matrix-matrix multiplication for different dimensions i. i runs from 5 to 500 with an increment of 5 and the matricies m1 and m2 are set up like this:

m1 = numpy.random.rand(i,i).astype(numpy.float32)
m2 = numpy.random.rand(i,i).astype(numpy.float32)

1. Numpy

The code used looks like this:

tNumpy = timeit.Timer("numpy.dot(m1, m2)", "import numpy; from __main__ import m1, m2")
rNumpy.append((i, tNumpy.repeat(20, 1)))

2. Python, calling BLAS through a shared object

With the function

_blaslib = ctypes.cdll.LoadLibrary("libblas.so")
def Mul(m1, m2, i, r):

    no_trans = c_char("n")
    n = c_int(i)
    one = c_float(1.0)
    zero = c_float(0.0)

    _blaslib.sgemm_(byref(no_trans), byref(no_trans), byref(n), byref(n), byref(n), 
            byref(one), m1.ctypes.data_as(ctypes.c_void_p), byref(n), 
            m2.ctypes.data_as(ctypes.c_void_p), byref(n), byref(zero), 
            r.ctypes.data_as(ctypes.c_void_p), byref(n))

the test code looks like this:

r = numpy.zeros((i,i), numpy.float32)
tBlas = timeit.Timer("Mul(m1, m2, i, r)", "import numpy; from __main__ import i, m1, m2, r, Mul")
rBlas.append((i, tBlas.repeat(20, 1)))

3. c++, calling BLAS through a shared object

Now the c++ code naturally is a little longer so I reduce the information to a minimum.
I load the function with

void* handle = dlopen("libblas.so", RTLD_LAZY);
void* Func = dlsym(handle, "sgemm_");

I measure the time with gettimeofday like this:

gettimeofday(&start, NULL);
f(&no_trans, &no_trans, &dim, &dim, &dim, &one, A, &dim, B, &dim, &zero, Return, &dim);
gettimeofday(&end, NULL);
dTimes[j] = CalcTime(start, end);

where j is a loop running 20 times. I calculate the time passed with

double CalcTime(timeval start, timeval end)
{
double factor = 1000000;
return (((double)end.tv_sec) * factor + ((double)end.tv_usec) - (((double)start.tv_sec) * factor + ((double)start.tv_usec))) / factor;
}

Results

The result is shown in the plot below:

Questions

1. Do you think my approach is fair, or are there some unnecessary overheads I can avoid?
2. Would you expect that the result would show such a huge discrepancy between the c++ and python approach? Both are using shared objects for their calculations.
3. Since I would rather use python for my program, what could I do to increase the performance when calling BLAS or LAPACK routines?

Download

The complete benchmark can be downloaded here. (J.F. Sebastian made that link possible^^)

Answer 1

I've run your benchmark. There is no difference between C++ and numpy on my machine:

Do you think my approach is fair, or are there some unnecessary overheads I can avoid?

It seems fair due to there is no difference in results.

Would you expect that the result would show such a huge discrepancy between the c++ and python approach? Both are using shared objects for their calculations.

No.

Since I would rather use python for my program, what could I do to increase the performance when calling BLAS or LAPACK routines?

Make sure that numpy uses optimized version of BLAS/LAPACK libraries on your system.