================= Memory management ================= .. _cuda-device-memory: 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: .. autofunction:: numba.cuda.device_array :noindex: .. autofunction:: numba.cuda.device_array_like :noindex: .. autofunction:: numba.cuda.to_device :noindex: Device arrays ------------- Device array references have the following methods. These methods are to be called in host code, not within CUDA-jitted functions. .. autoclass:: numba.cuda.cudadrv.devicearray.DeviceNDArray :members: copy_to_host, is_c_contiguous, is_f_contiguous, ravel, reshape :noindex: Pinned memory ============= .. autofunction:: numba.cuda.pinned :noindex: .. autofunction:: numba.cuda.pinned_array :noindex: Streams ======= .. autofunction:: numba.cuda.stream :noindex: CUDA streams have the following methods: .. autoclass:: numba.cuda.cudadrv.driver.Stream :members: synchronize, auto_synchronize :noindex: .. _cuda-shared-memory: 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. .. function:: numba.cuda.shared.array(shape, type) :noindex: 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 :ref:`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 :func:`.syncthreads`. .. function:: numba.cuda.syncthreads() :noindex: 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. .. seealso:: :ref:`Matrix multiplication example `. .. _cuda-local-memory: 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. .. function:: numba.cuda.local.array(shape, type) :noindex: 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). SmartArrays (experimental) ========================== Numba provides an Array-like data type that manages data movement to and from the device automatically. It can be used as drop-in replacement for `numpy.ndarray` in most cases, and is supported by Numba's JIT-compiler for both 'host' and 'cuda' target. .. comment: function:: numba.SmartArray(obj=None, copy=True, shape=None, dtype=None, order=None, where='host') .. autoclass:: numba.SmartArray :members: __init__, get, mark_changed Thus, `SmartArray` objects may be passed as function arguments to jit-compiled functions. Whenever a cuda.jit-compiled function is being executed, it will trigger a data transfer to the GPU (unless the data are already there). But instead of transferring the data back to the host after the function completes, it leaves the data on the device and merely updates the host-side if there are any external references to that. Thus, if the next operation is another invocation of a cuda.jit-compiled function, the data does not need to be transferred again, making the compound operation more efficient (and making the use of the GPU advantagous even for smaller data sizes). Deallocation Behavior ===================== Deallocation of all CUDA resources are tracked on a per-context basis. When the last reference to a device memory is dropped, the underlying memory is scheduled to be deallocated. The deallocation does not occur immediately. It is added to a queue of pending deallocations. This design has two benefits: 1. Resource deallocation API may cause the device to synchronize; thus, breaking any asynchronous execution. Deferring the deallocation could avoid latency in performance critical code section. 2. Some deallocation errors may cause all the remaining deallocations to fail. Continued deallocation errors can cause critical errors at the CUDA driver level. In some cases, this could mean a segmentation fault in the CUDA driver. In the worst case, this could cause the system GUI to freeze and could only recover with a system reset. When an error occurs during a deallocation, the remaining pending deallocations are cancelled. Any deallocation error will be reported. When the process is terminated, the CUDA driver is able to release all allocated resources by the terminated process. The deallocation queue is flushed automatically as soon as the following events occur: - An allocation failed due to out-of-memory error. Allocation is retried after flushing all deallocations. - The deallocation queue has reached its maximum size, which is default to 10. User can override by setting the environment variable `NUMBA_CUDA_MAX_PENDING_DEALLOCS_COUNT`. For example, `NUMBA_CUDA_MAX_PENDING_DEALLOCS_COUNT=20`, increases the limit to 20. - The maximum accumulated byte size of resources that are pending deallocation is reached. This is default to 20% of the device memory capacity. User can override by setting the environment variable `NUMBA_CUDA_MAX_PENDING_DEALLOCS_RATIO`. For example, `NUMBA_CUDA_MAX_PENDING_DEALLOCS_RATIO=0.5` sets the limit to 50% of the capacity. Sometimes, it is desired to defer resource deallocation until a code section ends. Most often, users want to avoid any implicit synchronization due to deallocation. This can be done by using the following context manager: .. autofunction:: numba.cuda.defer_cleanup