模板与滑动窗口
使用带边界单元的数据块处理邻域
模板与滑动窗口 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 Stencil Is
A stencil computes each output from a fixed neighborhood of inputs, like averaging a pixel with the cells around it.
The Sliding Window
As you move along the array, the input window slides by one. Consecutive outputs share most of their inputs, so reuse is huge.
Perfect for Tiling
Because neighbors overlap, a tile loaded once feeds many outputs. Stencils are a textbook case where tiling shines.
Meet the Halo
To compute the edges of a tile you also need a few elements just outside it. Those extra border cells are called the halo.
Why the Halo Exists
The first thread in a tile needs its left neighbor, which belongs to the previous tile. Without halo cells that read goes wrong.
Sizing the Halo
For a radius-r stencil you load r extra cells on each side. A 3-point blur has radius 1, so one halo cell per side suffices.
A Padded Tile
Declare shared memory as blockDim plus 2 * radius so it holds the interior plus both halos in one tidy buffer.
__shared__ float tile[BLOCK + 2 * RADIUS];Loading the Interior
Each thread first loads its own element into the tile at an offset of radius, leaving room for the left halo in front.
tile[threadIdx.x + RADIUS] = in[gid];Loading the Halos
The first few threads do double duty, also fetching the left and right halo cells before the block synchronizes.
Then Sync and Stencil
After __syncthreads, each thread reads its neighbors entirely from the tile, never touching global memory again for that step.
Reuse Multiplier
Every interior value now serves 2r + 1 outputs from one shared-memory load. That is exactly the redundant traffic tiling removes.
Quick Check
Why does a tiled stencil need halo cells?
Recap
Stencils slide overlapping windows, so a halo of r extra cells per side lets a tile serve every output with one load each. ✅
常见问题解答
「模板与滑动窗口」课时是免费的吗?
是的 — 「模板与滑动窗口」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。
「模板与滑动窗口」这节课中我会学到什么?
使用带边界单元的数据块处理邻域 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。
「模板与滑动窗口」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
- 数据复用问题
- 加载—同步—计算模式
- 模板与滑动窗口
- 处理边缘数据块