Note
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Compares matrix multiplication implementations with timeit¶
numpy has a very fast implementation of
matrix multiplication. There are many ways to be slower.
The following uses timeit
to compare implementations.
Compared implementations:
Preparation¶
import timeit
import numpy
from teachcompute.validation.cython.td_mul_cython import (
multiply_matrix,
c_multiply_matrix,
c_multiply_matrix_parallel,
c_multiply_matrix_parallel_transposed as cmulparamtr,
)
va = numpy.random.randn(150, 100).astype(numpy.float64)
vb = numpy.random.randn(100, 100).astype(numpy.float64)
ctx = {
"va": va,
"vb": vb,
"c_multiply_matrix": c_multiply_matrix,
"multiply_matrix": multiply_matrix,
"c_multiply_matrix_parallel": c_multiply_matrix_parallel,
"c_multiply_matrix_parallel_transposed": cmulparamtr,
}
Measures¶
numpy
res0 = timeit.timeit("va @ vb", number=100, globals=ctx)
print("numpy time", res0)
numpy time 0.011885600000823615
python implementation
res1 = timeit.timeit("multiply_matrix(va, vb)", number=10, globals=ctx)
print("python implementation", res1)
python implementation 7.265511200001129
cython implementation
res2 = timeit.timeit("c_multiply_matrix(va, vb)", number=100, globals=ctx)
print("cython implementation", res2)
cython implementation 0.1918974000000162
cython implementation parallelized
res3 = timeit.timeit("c_multiply_matrix_parallel(va, vb)", number=100, globals=ctx)
print("cython implementation parallelized", res3)
cython implementation parallelized 0.9288433000001532
cython implementation parallelized, AVX + transposed
res4 = timeit.timeit(
"c_multiply_matrix_parallel_transposed(va, vb)", number=100, globals=ctx
)
print("cython implementation parallelized avx", res4)
cython implementation parallelized avx 0.8376110999997763
Speed up…
numpy is 611.286868 faster than pure python.
numpy is 16.145369 faster than cython.
numpy is 78.148625 faster than parallelized cython.
numpy is 70.472765 faster than avx parallelized cython.
Total running time of the script: (0 minutes 9.253 seconds)