np.float64和np.double有什么区别? [英] What is the difference between np.float64 and np.double?

问题描述

我尝试运行以下代码来找出numpy中float64和double之间的区别.结果很有趣，因为double类型所花的时间几乎是与float64相乘所花费的时间的两倍.需要一些启示.

I tried running the following code to find out the difference between float64 and double in numpy. The result is interesting as type double takes almost double the time compared with time taken for multiplication with float64. Need some light on this.

import time
import numpy as np

datalen = 100000
times = 10000

a = np.random.rand(datalen)
b = np.random.rand(datalen)

da = np.float64(a)
db = np.float64(a)

dda = np.double(a)
ddb = np.double(b)

tic = time.time()
for k in range(times):
    dd = dda * ddb
toc = time.time()
print (toc - tic), 'time taken for double'

tic = time.time()
for k in range(times):
    d = da * db
toc = time.time()
print (toc - tic), 'time taken for float64'

推荐答案

我认为您正在将苹果与橙子进行比较.

I think you're comparing apples with oranges.

第一个席位基本上是a * b，但第二个席位是a * a.

The first bench is basically a * b but the second a * a.

我怀疑后者的缓存丢失要少得多.

I suspect much less cache misses for the latter.

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