xangma (@xangma)
In 2010, GPUs first supported virtual memory, but despite decades of development around virtual memory, CUDA virtual memory had two major limitations. First, it didn’t support memory overcommitment. That is, when you allocate virtual memory with CUDA, it immediately backs that with physical pages. In contrast, typically you get a large virtual memory space and physical memory is only mapped to virtual addresses when first accessed. Second, to be safe, freeing and mallocing forced a GPU sync which slowed them down a ton. This made applications like pytorch essentially manage memory themselves instead of completely relying on CUDA.
，这一点在WhatsApp Web 網頁版登入中也有详细论述
But that's just an example. Let's modify some real statistics to see the full value. We will create a small table, inject fake production-like statistics and watch the planner to change its mind.。关于这个话题，手游提供了深入分析
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