我一直在尝试使用Cython，我遇到了以下特殊情况：数组上的sum函数占用数组平均值的3倍。
以下是我的三个功能
cpdef FLOAT_t cython_sum(cnp.ndarray[FLOAT_t, ndim=1] A):
cdef double [:] x = A
cdef double sum = 0
cdef unsigned int N = A.shape[0]
for i in xrange(N):
sum += x[i]
return sum
cpdef FLOAT_t cython_avg(cnp.ndarray[FLOAT_t, ndim=1] A):
cdef double [:] x = A
cdef double sum = 0
cdef unsigned int N = A.shape[0]
for i in xrange(N):
sum += x[i]
return sum/N
cpdef FLOAT_t cython_silly_avg(cnp.ndarray[FLOAT_t, ndim=1] A):
cdef unsigned int N = A.shape[0]
return cython_avg(A)*N
以下是ipython中的运行时间
In [7]: A = np.random.random(1000000)
In [8]: %timeit np.sum(A)
1000 loops, best of 3: 906 us per loop
In [9]: %timeit np.mean(A)
1000 loops, best of 3: 919 us per loop
In [10]: %timeit cython_avg(A)
1000 loops, best of 3: 896 us per loop
In [11]: %timeit cython_sum(A)
100 loops, best of 3: 2.72 ms per loop
In [12]: %timeit cython_silly_avg(A)
1000 loops, best of 3: 862 us per loop
我无法在简单的cython_sum中考虑内存跳转。是因为一些内存分配？因为它们是从0到1的随机数。总和大约是500K。
由于line_profiler不能用于cython，我无法分析我的代码。