CuPy uses memory pool for memory allocations by default. The memory pool significantly improves the performance by mitigating the overhead of memory allocation and CPU/GPU synchronization.
There are two different memory pools in CuPy:
- Device memory pool (GPU device memory), which is used for GPU memory allocations.
- Pinned memory pool (non-swappable CPU memory), which is used during CPU-to-GPU data transfer.
When you monitor the memory usage (e.g., using
nvidia-smi for GPU memory or
ps for CPU memory), you may notice that memory not being freed even after the array instance become out of scope.
This is an expected behavior, as the default memory pool “caches” the allocated memory blocks.
See Low-Level CUDA Support for the details of memory management APIs.
Memory Pool Operations¶
The memory pool instance provides statistics about memory allocation.
To access the default memory pool instance, use
You can also free all unused memory blocks hold in the memory pool.
See the example code below for details:
import cupy import numpy mempool = cupy.get_default_memory_pool() pinned_mempool = cupy.get_default_pinned_memory_pool() # Create an array on CPU. # NumPy allocates 400 bytes in CPU (not managed by CuPy memory pool). a_cpu = numpy.ndarray(100, dtype=numpy.float32) print(a_cpu.nbytes) # 400 # You can access statistics of these memory pools. print(mempool.used_bytes()) # 0 print(mempool.total_bytes()) # 0 print(pinned_mempool.n_free_blocks()) # 0 # Transfer the array from CPU to GPU. # This allocates 400 bytes from the device memory pool, and another 400 # bytes from the pinned memory pool. The allocated pinned memory will be # released just after the transfer is complete. Note that the actual # allocation size may be rounded to larger value than the requested size # for performance. a = cupy.array(a_cpu) print(a.nbytes) # 400 print(mempool.used_bytes()) # 512 print(mempool.total_bytes()) # 512 print(pinned_mempool.n_free_blocks()) # 1 # When the array goes out of scope, the allocated device memory is released # and kept in the pool for future reuse. a = None # (or `del a`) print(mempool.used_bytes()) # 0 print(mempool.total_bytes()) # 512 print(pinned_mempool.n_free_blocks()) # 1 # You can clear the memory pool by calling `free_all_blocks`. mempool.free_all_blocks() pinned_mempool.free_all_blocks() print(mempool.used_bytes()) # 0 print(mempool.total_bytes()) # 0 print(pinned_mempool.n_free_blocks()) # 0
Limiting GPU Memory Usage¶
You can hard-limit the amount of GPU memory that can be allocated by using
CUPY_GPU_MEMORY_LIMIT environment variable (see Environment variables for details).
# Set the hard-limit to 1 GiB: # $ export CUPY_GPU_MEMORY_LIMIT="1073741824" # You can also specify the limit in fraction of the total amount of memory # on the GPU. If you have a GPU with 2 GiB memory, the following is # equivalent to the above configuration. # $ export CUPY_GPU_MEMORY_LIMIT="50%" import cupy print(cupy.get_default_memory_pool().get_limit()) # 1073741824
You can also set the limit (or override the value specified via the environment variable) using
In this way, you can use a different limit for each GPU device.
import cupy mempool = cupy.get_default_memory_pool() with cupy.cuda.Device(0): mempool.set_limit(size=1024**3) # 1 GiB with cupy.cuda.Device(1): mempool.set_limit(size=2*1024**3) # 2 GiB
CUDA allocates some GPU memory outside of the memory pool (such as CUDA context, library handles, etc.). Depending on the usage, such memory may take one to few hundred MiB. That will not be counted in the limit.
Changing Memory Pool¶
You can use your own memory allocator instead of the default memory pool by passing the memory allocation function to
The memory allocator function should take 1 argument (the requested size in bytes) and return
You can even disable the default memory pool by the code below. Be sure to do this before any other CuPy operations.
import cupy # Disable memory pool for device memory (GPU) cupy.cuda.set_allocator(None) # Disable memory pool for pinned memory (CPU). cupy.cuda.set_pinned_memory_allocator(None)