建立层次结构的心智模型
将数据匹配到合适的内存空间
建立层次结构的心智模型 是 CoddyKit 上的免费 CUDA Academy 课时。 这是第 4 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。
A Pyramid of Tradeoffs
Think of GPU memory as a pyramid. The top is tiny and lightning fast, while the base is huge but slow. Your job is to use each layer well.
Top: Registers
At the peak sit registers: per-thread, fastest, and very limited. Keep your hottest working values here whenever you possibly can.
Next: Shared Memory
Just below comes shared memory, an on-chip scratchpad visible to all threads in a block. It is your tool for fast intra-block teamwork.
Side Path: Constant Cache
Alongside sits the constant cache, ideal for small read-only values that a warp reads uniformly and broadcasts in one fetch.
Base: Global Memory
The wide base is global memory: gigabytes, visible to everyone, but high-latency. It holds your big inputs and outputs.
The Trap: Local Memory
Watch out for local memory. It sounds close by but lives in slow DRAM, used only when registers spill. Avoid relying on it.
Scope Decides the Space
Pick by who needs the data. One thread alone wants registers, a block working together wants shared memory, everyone wants global.
Lifetime Decides Too
Registers and shared memory vanish when a kernel ends, but global memory persists across launches. Match storage to how long data must live.
The Golden Rule
The winning pattern is load once from global, compute in fast on-chip memory, then write once back. This minimizes slow global memory traffic.
Capacity Costs Occupancy
Spending lots of registers or shared memory per block lets fewer blocks run at once. Occupancy is the balance you constantly tune.
Putting It Together
Great kernels deliberately route each piece of data to the right layer. That single habit is what separates slow code from fast CUDA code. 💪
Quick Check
Two threads in the SAME block need to share intermediate results. Which space fits best?
Recap: Match Data to Layer
You built a mental map: registers, shared, constant, and global trade speed for size and scope. Routing data to the right layer is the whole game. 🧠
常见问题解答
「建立层次结构的心智模型」课时是免费的吗？
是的 — 「建立层次结构的心智模型」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。
「建立层次结构的心智模型」这节课中我会学到什么？
将数据匹配到合适的内存空间 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 4 节课，共 4 节。
「建立层次结构的心智模型」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。