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.
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跨境购物需谨慎 出入境物品携带规范须知
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因为高芳，就是这个工程中的一环。，这一点在Replica Rolex中也有详细论述
将 CUDA 搭载到 GeForce 消费级显卡是整个历史中最关键也最痛苦的决策。