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将工作拆分为数据块

划分范围以平衡负载

将工作拆分为数据块 是 CoddyKit 上的免费 Mojo Academy 课时。这是第 2 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。

Why Chunk at All

Launching a thread per element is wasteful. Instead you split the range into a few larger chunks, one per worker.

A Chunk Is a Slice

Each chunk is just a contiguous slice of indices, like 0 to 99 or 100 to 199. One worker owns one slice.

Pick a Chunk Count

A common choice is one chunk per core. You ask the machine for that number with num_physical_cores.

var workers = num_physical_cores()

Compute the Chunk Size

Divide the total length by the worker count to get how many items each worker should handle.

var chunk = (n + workers - 1) // workers

Find a Chunk's Start

Inside the work function, the chunk index tells you where to begin. Multiply it by the chunk size.

var start = c * chunk

Clamp the End

The last chunk may run past the data, so clamp its end to the total length with min to stay in bounds.

var end = min(start + chunk, n)

Loop Inside the Chunk

Each worker runs a normal loop over just its own range. Inside the chunk it is plain, ordinary code.

for i in range(start, end):
    out[i] = heavy(i)

parallelize Over Chunks

Now you call parallelize with the number of chunks, not the number of elements. Each call processes one whole chunk.

parallelize[do_chunk](workers)

Balance the Load

Equal-sized chunks keep every core equally busy. If one chunk is far bigger, that worker finishes late.

More Chunks Than Cores

When some items cost more than others, using more, smaller chunks lets fast workers grab extra work and stay busy.

Tune by Measuring

The best chunk count depends on your data and machine. Try a few sizes and keep the one your benchmark likes.

Quick Check

You split a length-n array into one chunk per worker.

Recap

You divide the range into balanced chunks, clamp each end with min, loop inside a chunk, and call parallelize over the chunk count for even load. 🚀

常见问题解答

「将工作拆分为数据块」课时是免费的吗？

是的 — 「将工作拆分为数据块」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。

「将工作拆分为数据块」这节课中我会学到什么？

划分范围以平衡负载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 2 节课，共 4 节。

「将工作拆分为数据块」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。

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