Question

我最近偶然发现numba，并考虑用更优雅的autojitted python代码替换一些自制的C扩展。不幸的是，当我尝试第一个快速基准时，我并不高兴。看起来numba在这里并没有比普通的python做得好多少，虽然我本来期待几乎像C一样的性能：

from numba import jit, autojit, uint, double
import numpy as np
import imp
import logging
logging.getLogger('numba.codegen.debug').setLevel(logging.INFO)

def sum_accum(accmap, a):
    res = np.zeros(np.max(accmap) + 1, dtype=a.dtype)
    for i in xrange(len(accmap)):
        res[accmap[i]] += a[i]
    return res

autonumba_sum_accum = autojit(sum_accum)
numba_sum_accum = jit(double[:](int_[:], double[:]), 
                      locals=dict(i=uint))(sum_accum)

accmap = np.repeat(np.arange(1000), 2)
np.random.shuffle(accmap)
accmap = np.repeat(accmap, 10)
a = np.random.randn(accmap.size)

ref = sum_accum(accmap, a)
assert np.all(ref == numba_sum_accum(accmap, a))
assert np.all(ref == autonumba_sum_accum(accmap, a))

%timeit sum_accum(accmap, a)
%timeit autonumba_sum_accum(accmap, a)
%timeit numba_sum_accum(accmap, a)

accumarray = imp.load_source('accumarray', '/path/to/accumarray.py')
assert np.all(ref == accumarray.accum(accmap, a))

%timeit accumarray.accum(accmap, a)

这在我的机器上显示：

10 loops, best of 3: 52 ms per loop
10 loops, best of 3: 42.2 ms per loop
10 loops, best of 3: 43.5 ms per loop
1000 loops, best of 3: 321 us per loop

我正在运行pypi的最新numba版本，0.11.0。任何建议，如何修复代码，以便它与numba合理地运行？

Answer 1

我想通了自己。即使accmap的类型设置为int，numba也无法确定np.max(accmap)的结果类型。这在某种程度上减慢了一切，但修复很容易：

@autojit(locals=dict(reslen=uint))
def sum_accum(accmap, a):
    reslen = np.max(accmap) + 1
    res = np.zeros(reslen, dtype=a.dtype)
    for i in range(len(accmap)):
        res[accmap[i]] += a[i]
    return res

结果令人印象深刻，大约是C版本的2/3：

10000 loops, best of 3: 192 us per loop

Answer 2

@autojit
def numbaMax(arr):
    MAX = arr[0]
    for i in arr:
        if i > MAX:
            MAX = i
    return MAX

@autojit
def autonumba_sum_accum2(accmap, a):
    res = np.zeros(numbaMax(accmap) + 1)
    for i in xrange(len(accmap)):
        res[accmap[i]] += a[i]
    return res

10 loops, best of 3: 26.5 ms per loop <- original
100 loops, best of 3: 15.1 ms per loop <- with numba but the slow numpy max
10000 loops, best of 3: 47.9 µs per loop <- with numbamax

针对numba优化对numpy数组的访问

2 个答案: