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将循环向量化

将标量循环转换为向量运算

将循环向量化 是 CoddyKit 上的免费 Mojo Academy 课时。这是第 4 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Mojo Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Mojo Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

The Scalar Loop

A plain loop touches one element per step. It is clear but leaves your CPU's vector lanes mostly unused.

for i in range(n):
    out[i] = a[i] + b[i]

The Vector Idea

Vectorizing means each loop step handles a whole pack of elements instead of one. You do more work per iteration.

Load a Chunk

Instead of one value, you load several at once into a SIMD vector. That single load grabs a slice of your data.

var chunk = a.load[width=4](i)

Compute the Whole Chunk

With both chunks in SIMD form, add them in one element-wise step. The math covers every lane at once.

var sum = a.load[width=4](i) + b.load[width=4](i)

Store the Result

Write the whole SIMD result back in one move with a vector store. One write covers all the lanes you computed.

out.store[width=4](i, sum)

Step by the Width

A vectorized loop advances by the width, not by one. Each pass covers a full pack of elements.

for i in range(0, n, 4):
    pass

Let Mojo Drive It

Mojo's vectorize helper runs a closure across the range in vector steps and handles the bookkeeping for you.

from algorithm import vectorize

A vectorize Closure

You write a small parameterized function that processes one chunk. Mojo calls it with the right width as it sweeps the range.

fn work[w: Int](i: Int):
    out.store[width=w](i, a.load[width=w](i) + b.load[width=w](i))

Run the Vectorize

Call vectorize with your chunk function, the width, and the total size. It does the looping and the tail for you.

vectorize[work, 4](n)

Same Result, Far Faster

The vectorized loop gives identical output to the scalar one but moves through data in big steps, so it finishes sooner.

Measure to Confirm

Always time both versions. Vectorization usually wins big, but a benchmark proves the gain on your real data.

Quick Check

You replace a one-element-per-step loop with SIMD loads and stores. How should the loop advance?

Recap

Vectorizing loads and stores whole chunks, steps by the width, and lets vectorize handle the sweep and tail, giving the same result far faster. 🚀

常见问题解答

「将循环向量化」课时是免费的吗？

是的 — 「将循环向量化」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Mojo Academy 课程的其余内容，请升级到 CoddyKit PRO。 Mojo Academy 课程共包含 4 节课。

「将循环向量化」这节课中我会学到什么？

将标量循环转换为向量运算你通过在浏览器中直接运行的动手代码来练习 Mojo Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Mojo Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Mojo Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 4 节课，共 4 节。

「将循环向量化」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Mojo Academy 课中编写并运行代码吗？

能。每节 Mojo Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

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