Pytorch 比較CPU和GPU的運算速度

在跑神經網路的時候，gpu的作用是很明顯的。下面比較一下cpu跑和gpu跑的區別：

先用cpu跑：

import torch

import time
for i in
range(1
,10):
start_time = time.
time
() #返回當前語句執行時的時間點（單位秒）。
a = torch.
rand
(i*100
,1000
,1000
)   #生成i*
100個的1000行1000矩陣組成的張量，每個元素為0
-1之間的隨機值。
a = torch.
mul(a,a)
cul_end_time = time.
time()
- start_time
print
(cul_end_time)

0.441303014755249

0.8848202228546143
1.287822961807251
1.7319905757904053
2.177103281021118
2.555281162261963
2.9773526191711426
3.3875885009765625
3.9365322589874268
process finished with exit code 0

import torch

import time
for i in
range(1
,10):
start_time = time.
time
() #return current time in second.
a = torch.
rand
(i*100
,1000
,1000
)    
a = a.
cuda
()   #和上面的區別是，a.
cuda
()把張量a轉到gpu中，下面的運算就在gpu進行了
a = torch.
mul(a,a)
cul_end_time = time.
time()
- start_time
print
(cul_end_time)

526.2110984325409

1.0246520042419434
1.4614639282226562
1.8706181049346924
2.5586912631988525
3.071241617202759
3.5114991664886475
3.9417364597320557
4.653223752975464
process finished with exit code 0

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