cupy.cuda.MemoryPool¶
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class
cupy.cuda.MemoryPool¶ Memory pool for all devices on the machine.
A memory pool preserves any allocations even if they are freed by the user. Freed memory buffers are held by the memory pool as free blocks, and they are reused for further memory allocations of the same sizes. The allocated blocks are managed for each device, so one instance of this class can be used for multiple devices.
Note
When the allocation is skipped by reusing the pre-allocated block, it does not call
cudaMallocand therefore CPU-GPU synchronization does not occur. It makes interleaves of memory allocations and kernel invocations very fast.Note
The memory pool holds allocated blocks without freeing as much as possible. It makes the program hold most of the device memory, which may make other CUDA programs running in parallel out-of-memory situation.
Parameters: allocator (function) – The base CuPy memory allocator. It is used for allocating new blocks when the blocks of the required size are all in use. Methods
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free_all_blocks()¶ Release free blocks.
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free_all_free()¶ Release free blocks.
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malloc()¶ Allocates the memory, from the pool if possible.
This method can be used as a CuPy memory allocator. The simplest way to use a memory pool as the default allocator is the following code:
set_allocator(MemoryPool().malloc)
Parameters: size (int) – Size of the memory buffer to allocate in bytes. Returns: Pointer to the allocated buffer. Return type: MemoryPointer
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