我正在尝试优化一些执行大量顺序矩阵运算的代码。
我认为numpy.linalg.multi_dot
(docs here)将执行C或BLAS中的所有操作,因此它将比arr1.dot(arr2).dot(arr3)
等要快得多。
我真的很惊讶在笔记本上运行这段代码:
v1 = np.random.rand(2,2)
v2 = np.random.rand(2,2)
%%timeit
v1.dot(v2.dot(v1.dot(v2)))
The slowest run took 9.01 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 3.14 µs per loop
%%timeit
np.linalg.multi_dot([v1,v2,v1,v2])
The slowest run took 4.67 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 32.9 µs per loop
发现使用multi_dot
进行同样的操作大约要慢10倍。
我的问题是:
发布于 2017-08-24 11:12:30
这是因为您的测试矩阵太小且太规则;找出最快的评估顺序的开销可能会超过潜在的性能收益。
使用文档中的示例:
import numpy as snp
from numpy.linalg import multi_dot
# Prepare some data
A = np.random.rand(10000, 100)
B = np.random.rand(100, 1000)
C = np.random.rand(1000, 5)
D = np.random.rand(5, 333)
%timeit -n 10 multi_dot([A, B, C, D])
%timeit -n 10 np.dot(np.dot(np.dot(A, B), C), D)
%timeit -n 10 A.dot(B).dot(C).dot(D)
结果:
10 loops, best of 3: 12 ms per loop
10 loops, best of 3: 62.7 ms per loop
10 loops, best of 3: 59 ms per loop
multi_dot
通过计算标量乘法次数最少的最快乘法顺序来提高性能。
在上面的例子中,默认的规则乘法顺序((AB)C)D
被计算为A((BC)D)
--因此1000x100 @ 100x1000
乘法被减少为1000x100 @ 100x333
,从而至少减少了2/3
标量乘法。
可以通过测试来验证这一点
%timeit -n 10 np.dot(A, np.dot(np.dot(B, C), D))
10 loops, best of 3: 19.2 ms per loop
https://stackoverflow.com/questions/45852228
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