3.3. Memory management

3.3.1. Data transfer

Even though Numba can automatically transfer NumPy arrays to the device, it can only do so conservatively by always transferring device memory back to the host when a kernel finishes. To avoid the unnecessary transfer for read-only arrays, you can use the following APIs to manually control the transfer:

numba.cuda.to_device(array, stream=0)

Copy the Numpy array to device memory. A device array reference is returned which can be passed as argument to a kernel expecting the same kind of array.

If a CUDA stream is given, then the transfer will be made asynchronously as part as the given stream. Otherwise, the transfer is synchronous: the function returns after the copy is finished.

The lifetime of the allocated device memory is managed by Numba. Once it isn’t referred to anymore, it is automatically released.

3.3.1.1. Device arrays

Device array references have the following methods. These methods are to be called on the host, not on the device.

copy_to_host(array=None, stream=0)

Copy back contents of the device array to Numpy array on the host. If array is not given, a new array is allocated and returned.

If a CUDA stream is given, then the transfer will be made asynchronously as part as the given stream. Otherwise, the transfer is synchronous: the function returns after the copy is finished.

Example:

import numpy as np
from numba import cuda

arr = np.arange(1000)
d_arr = cuda.to_device(arr)

my_kernel[100, 100](d_arr)

result_array = d_arr.copy_to_host()

is_c_contiguous()

Return whether the array is C-contiguous.

is_f_contiguous()

Return whether the array is Fortran-contiguous.

ravel(order='C')

Flatten the array without changing its contents, similarly to numpy.ndarray.ravel().

reshape(*newshape, order='C')

Change the array’s shape without changing its contents, similarly to numpy.ndarray.reshape(). Example:

d_arr = d_arr.reshape(20, 50, order='F')

3.3.2. Streams

numba.cuda.stream()

Create and return a CUDA stream. A CUDA stream acts as a command queue for the device.

CUDA streams have the following method:

synchronize()

Wait for all commands in this stream to execute. This will commit any pending memory transfers.

3.3.3. Shared memory and thread synchronization

A limited amount of shared memory can be allocated on the device to speed up access to data, when necessary. That memory will be shared (i.e. both readable and writable) amongst all threads belonging to a given block and has faster access times than regular device memory. It also allows threads to cooperate on a given solution. You can think of it as a manually-managed data cache.

The memory is allocated once for the duration of the kernel, unlike traditional dynamic memory management.

numba.cuda.shared.array(shape, type)

Allocate a shared array of the given shape and type on the device. This function must be called on the device (i.e. from a kernel or device function). shape is either an integer or a tuple of integers representing the array’s dimensions. type is a Numba type of the elements needing to be stored in the array.

The returned array-like object can be read and written to like any normal device array (e.g. through indexing).

A common pattern is to have each thread populate one element in the shared array and then wait for all threads to finish using syncthreads().

numba.cuda.syncthreads()

Synchronize all threads in the same thread block. This function implements the same pattern as barriers in traditional multi-threaded programming: this function waits until all threads in the block call it, at which point it returns control to all its callers.

See also

Matrix multiplication example.

3.3.4. Local memory

Local memory is an area of memory private to each thread. Using local memory helps allocate some scratchpad area when scalar local variables are not enough. The memory is allocated once for the duration of the kernel, unlike traditional dynamic memory management.

numba.cuda.local.array(shape, type)

Allocate a local array of the given shape and type on the device. The array is private to the current thread. An array-like object is returned which can be read and written to like any standard array (e.g. through indexing).