分离的地址空间
了解主机指针为何在设备端无效
分离的地址空间 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。
Two Separate Memories
The CPU and GPU each own their own RAM. They do not share one address space, and that single fact shapes all of CUDA. 🧠
What an Address Means
A pointer is just a number naming a slot in memory. That slot only makes sense in the address space where it was created.
Host Pointers Are Local
A pointer from malloc names host memory. The GPU has no such slot, so handing it to a kernel points nowhere valid.
int* h = (int*)malloc(n * sizeof(int));Device Pointers Are Local Too
A pointer from cudaMalloc names device memory. The CPU cannot read it directly; dereferencing it on the host crashes.
int* d;
cudaMalloc(&d, n * sizeof(int));The Classic Mistake
Passing a host pointer into a kernel compiles fine but fails at runtime. The number is valid; the memory it names is not on the GPU.
Why It Compiles Anyway
Both pointers look like the same type to C++, so the compiler stays silent. The mismatch only bites when the GPU touches the address.
Bridging with cudaMemcpy
To move data between spaces you must copy it explicitly with cudaMemcpy. There is no automatic sharing across the divide.
cudaMemcpy(d, h, bytes, cudaMemcpyHostToDevice);A Naming Convention Helps
Many programmers prefix host pointers with h_ and device pointers with d_. It is a habit that prevents painful mix-ups.
float *h_a, *d_a;Kernels See Device Space
Inside a kernel every pointer you dereference must live in device memory. That is the only space the GPU threads can reach.
Unified Memory Hint
Later you will meet unified memory, one pointer valid on both sides. For now, keep host and device pointers strictly apart.
The Mental Model
Picture two rooms with no shared shelves. To use data in the other room you must carry a copy over, never just point across.
Quick Check
Let us confirm you understand separate address spaces.
Recap
You learned host and device live in separate address spaces, pointers are not interchangeable, and cudaMemcpy is how data crosses the gap. ✅
常见问题解答
「分离的地址空间」课时是免费的吗？
是的 — 「分离的地址空间」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。
「分离的地址空间」这节课中我会学到什么？
了解主机指针为何在设备端无效 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。
「分离的地址空间」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。