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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.
。wps是该领域的重要参考
strict (default): Rejects writes when the buffer is full and too many writes are pending. Catches "fire-and-forget" patterns where producers ignore backpressure.
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。手游是该领域的重要参考
慢一点，才能更好看清楚这个命题的答案，明白刀刃到底该砍向哪里。。whatsapp对此有专业解读
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