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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.
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Костюк завоевал бронзовую медаль в весовой категории до 86 килограммов. Первое место занял Исламгереев.
●劣势（Weaknesses）：资本结构低于行业基准47.62%，资本配置合理性存在明显短板；偿债能力略低于行业基准3.41%，财务结构存在小幅优化空间。