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GPU加速是通过大量并行计算来实现的。在一个GPU上你有大量的核心，每一个都不是很强大，但大量的核心在这里很重要。在
像PyTorch这样的框架可以尽可能多地并行计算。一般情况下，矩阵运算非常适合并行化，但也不总是能够并行化计算！在
在您的示例中，有一个循环：b = torch.ones(4,4).cuda()
for _ in range(1000000):
b += b
因为这些运算都是不可能的。如果你仔细想想，要计算下一个下一个b，你需要知道上一个上一个(或当前)^{的值。在
所以你有1000000的操作，但是每一个都必须一个接一个地计算。可能的并行化仅限于张量的大小。但在您的示例中，这个大小不是很大：
^{pr2}$
所以每次迭代只能并行化16操作(添加)。
由于CPU的很少，但更多的强大的内核，对于给定的示例来说，它只是快得多！在
但是，如果改变张量的大小，情况就会改变，那么PyTorch能够并行化更多的整体计算。我把迭代次数改为1000(因为我不想等那么久：)，但是你可以输入任何你喜欢的值，CPU和GPU之间的关系应该保持不变。在
以下是不同张量大小的结果：#torch.ones(4,4) - the size you used
CPU time = 0.00926661491394043
GPU time = 0.0431208610534668
#torch.ones(40,40) - CPU gets slower, but still faster than GPU
CPU time = 0.014729976654052734
GPU time = 0.04474186897277832
#torch.ones(400,400) - CPU now much slower than GPU
CPU time = 0.9702610969543457
GPU time = 0.04415607452392578
#torch.ones(4000,4000) - GPU much faster then CPU
CPU time = 38.088677167892456
GPU time = 0.044649362564086914
如你所见，在可以并行化东西的地方(这里添加了张量元素)，GPU变得非常强大。
对于给定的计算，GPU时间根本没有变化，GPU可以处理更多！
(只要内存没有用完：)
