寄存器与共享内存限制
了解资源如何限制常驻线程块数量
寄存器与共享内存限制 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。
The Resource Budget
Each SM owns a fixed pool of registers and shared memory. Every resident block must carve its share from these pools.
Registers Per Thread
Your kernel uses some number of registers per thread. Multiply by threads per block and you see one blocks register cost.
Registers Cap Blocks
If each thread needs many registers, fewer threads fit, so fewer blocks stay resident. Register-heavy kernels lose occupancy.
Seeing Register Use
Compile with this flag and nvcc prints registers per thread, letting you spot when a kernel is too register hungry.
nvcc -Xptxas -v vecadd.cuCapping Registers
You can force a ceiling with a launch bound so the compiler spends fewer registers and lets more warps stay resident.
__launch_bounds__(256, 4) __global__ void k() {}Shared Memory Per Block
Each block can request shared memory. The SM only fits as many blocks as its shared pool, often 48 to 100 KB, allows.
Shared Memory Caps Blocks
Ask for a large __shared__ tile and only one or two blocks fit per SM. Big tiles trade occupancy for data reuse.
The Tightest Limit Wins
The SM computes blocks allowed by registers, by shared memory, and by the warp cap. The smallest of these decides occupancy.
Register Spilling
When a thread needs more registers than allowed, extras spill to slow local memory. Spills can hurt more than low occupancy.
Tuning the Balance
Cutting registers or shared memory raises occupancy, but too aggressive a cut causes spills. The sweet spot is a balance.
Measure, Do Not Guess
Always read the actual register and shared usage from the compiler before tuning. Guessing usually picks the wrong knob.
Quick Check
Recall how the SM decides how many blocks can be resident.
Recap
You saw that registers and shared memory are fixed SM budgets, and the tightest limit caps occupancy. Watch for spills. 🧮
常见问题解答
「寄存器与共享内存限制」课时是免费的吗?
是的 — 「寄存器与共享内存限制」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。
「寄存器与共享内存限制」这节课中我会学到什么?
了解资源如何限制常驻线程块数量 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。
「寄存器与共享内存限制」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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- 寄存器与共享内存限制
- 占用率计算器 API
- 占用率并非全部