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如果对代码进行向量化，由于您正在将这些值当作字节进行处理，这意味着每条指令都要一次操作16 个值！

If you vectorize this code, since you are treating the values as bytes, that means that each instruction will operate on 16 values at once!

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它们与标准正交笛卡尔向量相关。

They are related to the orthonormal Cartesian vectors.

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谷歌的网页级别是特征向量中心性度量的一个变体。

Google's PageRank is a variant of the Eigenvector centrality measure.

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所以，这个向量场不是保守场。

So, this vector field is not conservative.

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那么当然知道它的法向量。

Well, you know the normal vector.

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现在，梯度向量是什么呢？

Now, what's the gradient vector?

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那么如果我选择了任何位于切平面的向量。

Then, if I choose any vector in that tangent plane.

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那相当于法向量指向上。

That corresponds to normal vector pointing up.

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那么法向量又是什么呢？

What is the normal vector?

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故我们是把位置向量，分解为简单向量的和。

So, we want to decompose the position vector into a sum of simpler vectors.

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现在我们来考虑向量ab和向量。

BP Now, let's think about vector ab and vector BP.

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那么向量在这，--，这向量是。

OP So, this vector here -- This vector here is vector OP.

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什么是自动化向量？

What is auto-vectorization?

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这些则包括向量。

These involve vector quantities.

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它不是单位向量。

It is not a unit vector.

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向量BP是什么？

What about vector BP?

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这一向量就称为法线向量或简称为法线。

This vector is called a normal vector or normal for short.

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有一个向量场来描述每一个点上的向量。

We have a vector field that gives us a vector at every point.

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所以，梯度向量是垂直的。

So, the gradient is perpendicular.

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速度向量，是位置向量关于时间的导数。

So, the velocity vector is the derivative of a position vector with respect to time.

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