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将工作捕获到图中

将流记录为 CUDA 图

将工作捕获到图中 是 CoddyKit 上的免费 CUDA 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 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。

What a CUDA Graph Is

A CUDA graph records a whole sequence of kernels and copies as one reusable unit of work you can submit again and again.

Launches Add Up

Each individual kernel launch has a tiny CPU overhead. Across thousands of launches per iteration, that cost becomes real.

Define Once, Run Many

A graph lets you describe the work once, then replay it cheaply, paying the setup cost a single time.

Stream Capture Mode

The easiest way to build one is stream capture: you record the normal launches you already issue into a stream.

cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);

Issue Work Normally

Between begin and end, you launch kernels and copies as usual. Nothing runs yet; the operations are recorded, not executed.

kernelA<<<g, b, 0, stream>>>(d);
kernelB<<<g, b, 0, stream>>>(d);

End the Capture

You stop recording with cudaStreamEndCapture, which hands back a cudaGraph_t describing all the work.

cudaGraph_t graph;
cudaStreamEndCapture(stream, &graph);

Dependencies Inferred

Capture figures out the dependencies between operations automatically from how you used the stream.

Instantiate Before Running

A graph is just a description. You compile it into a runnable graph exec object before you can launch it.

cudaGraphExec_t exec;
cudaGraphInstantiate(&exec, graph, 0);

Building Graphs by Hand

You can also add nodes explicitly with the graph API for full control over structure and edges.

cudaGraphAddKernelNode(&n, graph, deps, 1, &params);

Multiple Streams, One Graph

Capture can span several streams, so independent branches that run concurrently all land in the same graph.

Reuse Across Many Iterations

Once instantiated, the same exec object is launched repeatedly, which is exactly where graphs earn their keep.

Quick Check

What does cudaStreamEndCapture produce?

Recap: Capturing Graphs

Wrap launches in stream capture to record a cudaGraph_t, instantiate it once, and capture infers the dependencies for you. Great progress! 🎉

常见问题解答

「将工作捕获到图中」课时是免费的吗？

是的 — 「将工作捕获到图中」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。

「将工作捕获到图中」这节课中我会学到什么？

将流记录为 CUDA 图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 3 节课，共 4 节。

「将工作捕获到图中」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

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