A naive version of dot product based on cython. It's multi-threading.
Python is a good programming language but is too slow.
GIL(Global Interpreter Lock) bothers us too much when we want to use more CPUs in our codes.
So I write a tool with cython(a optimising static compiler which can be used to write C extensions for python).
It's easy to make the job run parallelly with cython.parallel.
BLAS(Basic Linear Algebra Subprograms) is also used in the code too.
It's no doubt that python has already had a powerful lib numpy and scipy to do scientific caculations.
numpy.dot is also support multi-threading if you compiled it with optimized BLAS library like ATLAS or OpenBLAS.
The code is only written because I want to practise how to write C extensions for python.
The code is in fast_utils.pyx. There are two functions: fast_dot() and fast_dot_blas().
The latter one uses blas to do calculations faster. We should remember that
the two input matrix A and B shouldn't have been numpy.transpose() if we want to get the answer
right. If we want to do A * B we can call fast_dot(A,B,Result,n_jobs=2) or
fast_dot_blas(A,B_Transpose,n_jobs=2).
The input matrix should be like this numpy.array([[1,2],[2,4]], dtype=float32).
Run test_utils.py will make you more clear.
- Jobs: dot product between
A(2000,3000)andB(3000,2000) - Env: ubuntu 14, x64, Intel(R) Core(TM) i7 CPU, 2.93GHz
- Some dependencies: install python, cython(0.20.2 or higher), numpy, scipy
- Compiling:
python setup build - Copy the lib to your work space:
cp build/lib.linux-x86_64-2.7/fast_utils.so ./your_workspace - usage:
from fast_utils import fast_dotorfrom fast_utils import fast_dot_blas
If you have any questions or suggestions, feel free to contact me(shaoyf2011@gmail.com).
| method | core(s) | time |
|---|---|---|
| np.dot | 1 | 10.85s |
| fast_dot | 1 | 60.97s |
| fast_dot_blas | 1 | 10.74s |
| fast_dot | 8 | 22.50s |
| fast_dot_blas | 8 | 2.06s |