July 2018
Intermediate to advanced
310 pages
8h 21m
Chinese
Content preview from 深度学习入门 : 基于Python的理论与实现
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8.3 深度学习的高速化
249
图8-14 AlexNet 的forward 处理中各层的时间比：左边是使用GPU 的情况，右边是使
用 CPU 的情况。图中的“conv”对应卷积层，“pool”对应池化层，“fc”对应全
连接层，“norm”对应正规化层（引用自文献[26]）
8.3.2
基于GPU的高速化
GPU
原本是作为图像专用的显卡使用的，但最近不仅用于图像处理，
也用于通用的数值计算。由于
GPU
可以高速地进行并行数值计算，因此
GPU 计算的目标就是将这种压倒性的计算能力用于各种用途。所谓
GPU
计算，
是指基于
GPU
进行通用的数值计算的操作。
深度学习中需要进行大量的乘积累加运算（或者大型矩阵的乘积运算）。
这种大量的并行运算正是
GPU
所擅长的（反过来说，
CPU
比较擅长连续的、
复杂的计算）。因此，与使用单个
CPU
相比，使用
GPU
进行深度学习的运算
可以达到惊人的高速化。下面我们就来看一下基于
GPU
可以实现多大程度的
高速化。图8
-
15 是基于
CPU
和
GPU
进行
AlexNet
的学习时分别所需的时间。
从图中可知，使用
CPU
要花40 天以上的时间，而使用
GPU
则可以将时
间缩短至6 天。此外，还可以看出，通过使用
cuDNN
这个最优化的库，可
以进一步实现高速化。
GPU
主要由
NVIDIA
和
AMD
两家公司提供。虽然两家的
GPU
都可以
用于通用的数值计算，但与深度学习比较“亲近”的是
NVIDIA
的
GPU
。实
际上，大多数深度学习框架只受益于
NVIDIA
的
GPU
。这是因为深度学习
的框架中使用了
NVIDIA ...
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