并行化并调优核心
添加线程并针对机器自动调优
并行化并调优核心 是 CoddyKit 上的免费 Mojo Academy 课时。 这是第 3 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。
From Fast to Faster
Your kernel already beats Python on one core. Now spread it across all your cores and let Mojo tune it for the machine. ⚡
Why Go Parallel
Modern CPUs have many cores sitting idle. Parallelism lets each core work on part of the data at the same time for a big speedup.
Meet parallelize
Mojo's parallelize runs loop chunks across threads for you. You give it the work and how many pieces to split it into.
from algorithm import parallelizeWrap Work in a Closure
Express one chunk of work as a small function. parallelize calls it once per chunk, each on its own core.
fn work(chunk: Int):
process(chunk)
parallelize[work](num_chunks)Split the Range
Divide the data into roughly equal chunks, one per core. Balanced pieces keep every thread busy instead of one lagging behind.
var per_chunk = n // num_chunksStack SIMD on Threads
Keep your SIMD math inside each chunk. Now you get two wins at once: many cores, each crunching vectors per instruction.
Avoid Data Races
Two threads writing the same spot causes a data race. Give each chunk its own output slice so writes never collide.
Expose Tunable Knobs
Turn tile size and SIMD width into parameters. With knobs in place, you can try many settings without rewriting the kernel.
fn kernel[tile: Int, width: Int]():
passLet Autotuning Search
The best settings differ per machine. Autotuning tries candidate parameter values, times each, and keeps the fastest combination.
Lock In the Winner
Once you know the best tile and width, fix them as the chosen config. The kernel is now specialized for your hardware.
Verify Under Parallelism
Run the tuned, threaded kernel and compare to the baseline. Correctness must still hold once many cores write at once.
Quick Check
Pick the real risk when parallelizing the core.
Recap
You used parallelize over balanced chunks, kept SIMD inside each, avoided data races, then autotuned the knobs. The core is now multi-core fast. 🎯
常见问题解答
「并行化并调优核心」课时是免费的吗？
是的 — 「并行化并调优核心」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。
「并行化并调优核心」这节课中我会学到什么？
添加线程并针对机器自动调优 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 3 节课，共 4 节。
「并行化并调优核心」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。