Built-in Utilities ================== Automatic Initialization ------------------------ .. module:: pycuda.autoinit The module :mod:`pycuda.autoinit`, when imported, automatically performs all the steps necessary to get CUDA ready for submission of compute kernels. It uses :func:`pycuda.tools.make_default_context` to create a compute context. .. data:: device An instance of :class:`pycuda.driver.Device` that was used for automatic initialization. .. data:: context A default-constructed instance of :class:`pycuda.driver.Context` on :data:`device`. This context is created by calling :func:`pycuda.tools.make_default_context`. .. module:: pycuda.autoprimaryctx The module :mod:`pycuda.autoprimaryctx` is similar to :mod:`pycuda.autoinit`, except that it retains the device primary context instead of creating a new context in :func:`pycuda.tools.make_default_context`. Notably, it also has ``device`` and ``context`` attributes. Choice of Device ---------------- .. module:: pycuda.tools .. function:: make_default_context() Return a :class:`pycuda.driver.Context` instance chosen according to the following rules: * If the environment variable :envvar:`CUDA_DEVICE` is set, its integer value is used as the device number. * If the file :file:`.cuda-device` is present in the user's home directory, the integer value of its contents is used as the device number. * Otherwise, all available CUDA devices are tried in a round-robin fashion. An error is raised if this does not lead to a usable context. .. versionadded: 0.94 .. function:: get_default_device(default=0) Deprecated. Use :func:`make_default_context`. Return a :class:`pycuda.driver.Device` instance chosen according to the following rules: * If the environment variable :envvar:`CUDA_DEVICE` is set, its integer value is used as the device number. * If the file :file:`.cuda-device` is present in the user's home directory, the integer value of its contents is used as the device number. * Otherwise, `default` is used as the device number. .. versionchanged: 0.94 Deprecated. Kernel Caching -------------- .. function:: context_dependent_memoize(func) This decorator caches the result of the decorated function, *if* a subsequent occurs in the same :class:`pycuda.driver.Context`. This is useful for caching of kernels. .. function:: clear_context_caches() Empties all context-dependent memoization caches. Also releases all held reference contexts. If it is important to you that the program detaches from its context, you might need to call this function to free all remaining references to your context. Testing ------- .. function:: mark_cuda_test(func) This function, meant for use with :mod:`py.test`, will mark *func* with a "cuda" tag and make sure it has a CUDA context available when invoked. Device Metadata and Occupancy ----------------------------- .. class:: DeviceData(dev=None) Gives access to more information on a device than is available through :meth:`pycuda.driver.Device.get_attribute`. If `dev` is `None`, it defaults to the device returned by :meth:`pycuda.driver.Context.get_device`. .. attribute:: max_threads .. attribute:: warp_size .. attribute:: warps_per_mp .. attribute:: thread_blocks_per_mp .. attribute:: registers .. attribute:: shared_memory .. attribute:: smem_granularity The number of threads that participate in banked, simultaneous access to shared memory. .. attribute:: smem_alloc_granularity The size of the smallest possible (non-empty) shared memory allocation. .. method:: align_bytes(word_size=4) The distance between global memory base addresses that allow accesses of word-size `word_size` bytes to get coalesced. .. method:: align(bytes, word_size=4) Round up `bytes` to the next alignment boundary as given by :meth:`align_bytes`. .. method:: align_words(word_size) Return `self.align_bytes(word_size)/word_size`, while checking that the division did not yield a remainder. .. method:: align_dtype(elements, dtype_size) Round up `elements` to the next alignment boundary as given by :meth:`align_bytes`, where each element is assumed to be `dtype_size` bytes large. .. UNDOC coalesce .. staticmethod:: make_valid_tex_channel_count(size) Round up `size` to a valid texture channel count. .. class:: OccupancyRecord(devdata, threads, shared_mem=0, registers=0) Calculate occupancy for a given kernel workload characterized by * thread count of `threads` * shared memory use of `shared_mem` bytes * register use of `registers` 32-bit registers .. attribute:: tb_per_mp How many thread blocks execute on each multiprocessor. .. attribute:: limited_by What :attr:`tb_per_mp` is limited by. One of `"device"`, `"warps"`, `"regs"`, `"smem"`. .. attribute:: warps_per_mp How many warps execute on each multiprocessor. .. attribute:: occupancy A `float` value between 0 and 1 indicating how much of each multiprocessor's scheduling capability is occupied by the kernel. .. _mempool: Memory Pools ------------ The functions :func:`pycuda.driver.mem_alloc` and :func:`pycuda.driver.pagelocked_empty` can consume a fairly large amount of processing time if they are invoked very frequently. For example, code based on :class:`pycuda.gpuarray.GPUArray` can easily run into this issue because a fresh memory area is allocated for each intermediate result. Memory pools are a remedy for this problem based on the observation that often many of the block allocations are of the same sizes as previously used ones. Then, instead of fully returning the memory to the system and incurring the associated reallocation overhead, the pool holds on to the memory and uses it to satisfy future allocations of similarly-sized blocks. The pool reacts appropriately to out-of-memory conditions as long as all memory allocations are made through it. Allocations performed from outside of the pool may run into spurious out-of-memory conditions due to the pool owning much or all of the available memory. Device-based Memory Pool ^^^^^^^^^^^^^^^^^^^^^^^^ .. class:: PooledDeviceAllocation An object representing a :class:`DeviceMemoryPool`-based allocation of linear device memory. Once this object is deleted, its associated device memory is freed. :class:`PooledDeviceAllocation` instances can be cast to :class:`int` (and :class:`long`), yielding the starting address of the device memory allocated. .. method:: free Explicitly return the memory held by *self* to the associated memory pool. .. method:: __len__ Return the size of the allocated memory in bytes. .. class:: DeviceMemoryPool A memory pool for linear device memory as allocated using :func:`pycuda.driver.mem_alloc`. (see :ref:`mempool`) .. attribute:: held_blocks The number of unused blocks being held by this pool. .. attribute:: active_blocks The number of blocks in active use that have been allocated through this pool. .. attribute:: managed_bytes "Managed" memory is "active" and "held" memory. .. versionadded: 2021.1 .. attribute:: active_bytes "Active" bytes are bytes under the control of the application. This may be smaller than the actual allocated size reflected in :attr:`managed_bytes`. .. versionadded: 2021.1 .. method:: allocate(size) Return a :class:`PooledDeviceAllocation` of *size* bytes. .. method:: free_held Free all unused memory that the pool is currently holding. .. method:: stop_holding Instruct the memory to start immediately freeing memory returned to it, instead of holding it for future allocations. Implicitly calls :meth:`free_held`. This is useful as a cleanup action when a memory pool falls out of use. Memory Pool for pagelocked memory ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. class:: PooledHostAllocation An object representing a :class:`PageLockedMemoryPool`-based allocation of linear device memory. Once this object is deleted, its associated device memory is freed. .. method:: free Explicitly return the memory held by *self* to the associated memory pool. .. method:: __len__ Return the size of the allocated memory in bytes. .. class:: PageLockedAllocator(flags=0) Specifies the set of :class:`pycuda.driver.host_alloc_flags` used in its associated :class:`PageLockedMemoryPool`. .. class:: PageLockedMemoryPool(allocator=PageLockedAllocator()) A memory pool for pagelocked host memory as allocated using :func:`pycuda.driver.pagelocked_empty`. (see :ref:`mempool`) .. attribute:: held_blocks The number of unused blocks being held by this pool. .. attribute:: active_blocks The number of blocks in active use that have been allocated through this pool. .. method:: allocate(shape, dtype, order="C") Return an uninitialized ("empty") :class:`numpy.ndarray` with the given *shape*, *dtype*, and *order*. This array will be backed by a :class:`PooledHostAllocation`, which can be found as the ``.base`` attribute of the array. .. method:: free_held Free all unused memory that the pool is currently holding. .. method:: stop_holding Instruct the memory to start immediately freeing memory returned to it, instead of holding it for future allocations. Implicitly calls :meth:`free_held`. This is useful as a cleanup action when a memory pool falls out of use.