21:41, 8 марта 2026Мир
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By default, freeing memory in CUDA is expensive because it does a GPU sync. Because of this, PyTorch avoids freeing and mallocing memory through CUDA, and tries to manage it itself. When blocks are freed, the allocator just keeps them in their own cache. The allocator can then use the free blocks in the cache when something else is allocated. But if these blocks are fragmented and there isn’t a large enough cache block and all GPU memory is already allocated, PyTorch has to free all the allocator cached blocks then allocate from CUDA, which is a slow process. This is what our program is getting blocked by. This situation might look familiar if you’ve taken an operating systems class.，详情可参考手游
“最终的政治影响，很可能要到今年11月的中期选举才能真正体现出来。”赵穗生说。
"I wanted to meet that challenge and portray her differently from how she's traditionally been seen.