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“Nobody knows what’s going on. Everybody’s really confused,” says Lindsay. “The messages are coming so fast in that channel. It’s just absolute chaos. ‘Help, please. What do I do? What am I supposed to do? Where do I go? Can I get started tasking? Am I supposed to redo all the assessments that I’ve done before?’”
。新收录的资料对此有专业解读
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.，详情可参考新收录的资料
Марина Совина (ночной редактор)