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

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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:

enter image description here

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^^)

👻 Read also: what is the best laptop for engineering students?

We hope this article has helped you to resolve the problem. Apart from Benchmarking (python vs. c++ using BLAS) and (numpy), check other __main__ Python module-related topics.

Want to excel in Python? See our review of the best Python online courses 2023. If you are interested in Data Science, check also how to learn programming in R.

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Anna Ungerschaft

Berlin | 2023-02-01

http Python module is always a bit confusing 😭 Benchmarking (python vs. c++ using BLAS) and (numpy) is not the only problem I encountered. I am just not quite sure it is the best method

Xu Danburry

London | 2023-02-01

I was preparing for my coding interview, thanks for clarifying this - Benchmarking (python vs. c++ using BLAS) and (numpy) in Python is not the simplest one. Checked yesterday, it works!

Davies Robinson

Texas | 2023-02-01

StackOverflow is always a bit confusing 😭 Benchmarking (python vs. c++ using BLAS) and (numpy) is not the only problem I encountered. Will use it in my bachelor thesis

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