我目前正在尝试遵循一个简单的示例,以使用 cython 的 prange 并行化循环。我已经安装了允许使用 openmp 的 OpenBlas 0.2.14,并针对 openblas 从源代码编译了 numpy 1.10.1 和 scipy 0.16。为了测试库的性能,我遵循以下示例: http: //nealhughes.net/parallelcomp2/。要计时的功能是从站点复制的:
import numpy as np
from math import exp
from libc.math cimport exp as c_exp
from cython.parallel import prange,parallel
def array_f(X):
Y = np.zeros(X.shape)
index = X > 0.5
Y[index] = np.exp(X[index])
return Y
def c_array_f(double[:] X):
cdef int N = X.shape[0]
cdef double[:] Y = np.zeros(N)
cdef int i
for i in range(N):
if X[i] > 0.5:
Y[i] = c_exp(X[i])
else:
Y[i] = 0
return Y
def c_array_f_multi(double[:] X):
cdef int N = X.shape[0]
cdef double[:] Y = np.zeros(N)
cdef int i
with nogil, parallel():
for i in prange(N):
if X[i] > 0.5:
Y[i] = c_exp(X[i])
else:
Y[i] = 0
return Y
该代码的作者报告了以下 4 个内核的加速:
from thread_demo import *
import numpy as np
X = -1 + 2*np.random.rand(10000000)
%timeit array_f(X)
1 loops, best of 3: 222 ms per loop
%timeit c_array_f(X)
10 loops, best of 3: 87.5 ms per loop
%timeit c_array_f_multi(X)
10 loops, best of 3: 22.4 ms per loop
当我在我的机器(带有 osx 10.10 的 macbook pro)上运行这些示例时,我得到以下导出时间OMP_NUM_THREADS=1
In [1]: from bla import *
In [2]: import numpy as np
In [3]: X = -1 + 2*np.random.rand(10000000)
In [4]: %timeit c_array_f(X)
10 loops, best of 3: 89.7 ms per loop
In [5]: %timeit c_array_f_multi(X)
1 loops, best of 3: 343 ms per loop
并且对于OMP_NUM_THREADS=4
In [1]: from bla import *
In [2]: import numpy as np
In [3]: X = -1 + 2*np.random.rand(10000000)
In [4]: %timeit c_array_f(X)
10 loops, best of 3: 89.5 ms per loop
In [5]: %timeit c_array_f_multi(X)
10 loops, best of 3: 119 ms per loop
我在 openSuse 机器上看到了同样的行为,因此我提出了问题。作者如何在我的 2 个系统上的 4 个线程上运行相同的代码时获得 4 倍的速度。
生成的设置脚本*.c & .so
也与博客中使用的相同。
from distutils.core import setup
from Cython.Build import cythonize
from distutils.extension import Extension
from Cython.Distutils import build_ext
import numpy as np
ext_modules=[
Extension("bla",
["bla.pyx"],
libraries=["m"],
extra_compile_args = ["-O3", "-ffast-math","-march=native", "-fopenmp" ],
extra_link_args=['-fopenmp'],
include_dirs = [np.get_include()]
)
]
setup(
name = "bla",
cmdclass = {"build_ext": build_ext},
ext_modules = ext_modules
)
如果有人可以向我解释为什么会发生这种情况,那就太好了。