CUDA Academy · 课时

跨 GPU 划分工作

域分解策略

跨 GPU 划分工作 是 CoddyKit 上的免费 CUDA 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 CUDA Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 CUDA 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。

Many GPUs, One Job

Two GPUs can finish a job in roughly half the time, but only if you split the work. The art is partitioning: deciding which GPU handles which part.

Domain Decomposition

The classic strategy is to cut the data, not the code. With domain decomposition each GPU gets its own slice of the array or grid to process.

Slicing an Array

For a 1D array, just divide its length. Give the first chunk of elements to GPU 0 and the next chunk to GPU 1, and so on.

int chunk = n / count;

Computing Each Offset

Every GPU needs the start of its slice. The offset for device d is simply d times the chunk size, marking where its data begins.

int offset = d * chunk;

Allocate Per Device

Each GPU needs its own buffer. Set the device, then cudaMalloc space just for that card's slice instead of the whole array.

cudaSetDevice(d);
cudaMalloc(&dptr[d], chunk * sizeof(float));

Copy Only the Slice

Upload to each GPU only the portion it owns. Copy from host[offset] into that device's buffer so no card holds data it will not touch.

Launch on Every Device

Loop over the GPUs, set each current, and launch the kernel on its slice. The launches are asynchronous, so all cards start working in parallel.

Gather the Results Back

When kernels finish, copy each device's output back into the right spot of the host array using its offset. The pieces reassemble into one result.

Mind the Leftover

If n does not divide evenly, the last GPU must handle the remainder. Give it the extra elements so nothing in the array is skipped.

int last = n - offset;

Watch the Boundaries

Stencil and neighbor operations read across slice edges. Those halo regions must be shared between GPUs, or results at the borders go wrong.

Balance the Load

If one GPU is faster, an even split wastes it. Good load balancing gives the stronger card a bigger slice so both finish at the same time.

Quick Check

Recall the standard way to spread one large dataset across several GPUs.

Recap

You split data into slices, allocate and copy per device, launch on each, then gather results. Mind the remainder and halos. Next: copying directly GPU to GPU. ✨

常见问题解答

「跨 GPU 划分工作」课时是免费的吗？

是的 — 「跨 GPU 划分工作」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 CUDA Academy 课程的其余内容，请升级到 CoddyKit PRO。 CUDA Academy 课程共包含 4 节课。

「跨 GPU 划分工作」这节课中我会学到什么？

域分解策略你通过在浏览器中直接运行的动手代码来练习 CUDA 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 CUDA Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 CUDA 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。

「跨 GPU 划分工作」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 CUDA Academy 课中编写并运行代码吗？

能。每节 CUDA 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

← 返回 CUDA Academy