Built-in Utilities ================== .. module:: pyopencl.tools .. _memory-pools: Memory Pools ------------ The constructor :func:`pyopencl.Buffer` can consume a fairly large amount of processing time if it is invoked very frequently. For example, code based on :class:`pyopencl.array.Array` 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. Using :class:`pyopencl.array.Array` instances with a :class:`MemoryPool` is not complicated:: mem_pool = pyopencl.tools.MemoryPool(pyopencl.tools.ImmediateAllocator(queue)) a_dev = cl_array.arange(queue, 2000, dtype=np.float32, allocator=mem_pool) .. class:: PooledBuffer An object representing a :class:`MemoryPool`-based allocation of device memory. Once this object is deleted, its associated device memory is returned to the pool. This supports the same interface as :class:`pyopencl.Buffer`. .. class:: DeferredAllocator(context, mem_flags=pyopencl.mem_flags.READ_WRITE) *mem_flags* takes its values from :class:`pyopencl.mem_flags` and corresponds to the *flags* argument of :class:`pyopencl.Buffer`. DeferredAllocator has the same semantics as regular OpenCL buffer allocation, i.e. it may promise memory to be available that may (in any call to a buffer-using CL function) turn out to not exist later on. (Allocations in CL are bound to contexts, not devices, and memory availability depends on which device the buffer is used with.) .. versionchanged :: 2013.1 :class:`CLAllocator` was deprecated and replaced by :class:`DeferredAllocator`. .. method:: __call__(size) Allocate a :class:`pyopencl.Buffer` of the given *size*. .. versionchanged :: 2020.2 The allocator will succeed even for allocations of size zero, returning *None*. .. class:: ImmediateAllocator(queue, mem_flags=pyopencl.mem_flags.READ_WRITE) *mem_flags* takes its values from :class:`pyopencl.mem_flags` and corresponds to the *flags* argument of :class:`pyopencl.Buffer`. :class:`ImmediateAllocator` will attempt to ensure at allocation time that allocated memory is actually available. If no memory is available, an out-of-memory error is reported at allocation time. .. versionadded:: 2013.1 .. method:: __call__(size) Allocate a :class:`pyopencl.Buffer` of the given *size*. .. versionchanged :: 2020.2 The allocator will succeed even for allocations of size zero, returning *None*. .. class:: MemoryPool(allocator[, leading_bits_in_bin_id]) A memory pool for OpenCL device memory. *allocator* must be an instance of one of the above classes, and should be an :class:`ImmediateAllocator`. The memory pool assumes that allocation failures are reported by the allocator immediately, and not in the OpenCL-typical deferred manner. .. note:: The current implementation of the memory pool will retain allocated memory after it is returned by the application and keep it in a bin identified by the leading *leading_bits_in_bin_id* bits of the allocation size. To ensure that allocations within each bin are interchangeable, allocation sizes are rounded up to the largest size that shares the leading bits of the requested allocation size. The current default value of *leading_bits_in_bin_id* is four, but this may change in future versions and is not guaranteed. *leading_bits_in_bin_id* must be passed by keyword, and its role is purely advisory. It is not guaranteed that future versions of the pool will use the same allocation scheme and/or honor *leading_bits_in_bin_id*. .. versionchanged:: 2019.1 Current bin allocation behavior documented, *leading_bits_in_bin_id* added. .. 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(size) Return a :class:`PooledBuffer` of the given *size*. .. method:: __call__(size) Synonym for :meth:`allocate` to match :class:`CLAllocator` interface. .. versionadded: 2011.2 .. 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. CL-Object-dependent Caching --------------------------- .. autofunction:: first_arg_dependent_memoize .. autofunction:: clear_first_arg_caches Testing ------- .. function:: pytest_generate_tests_for_pyopencl(metafunc) Using the line:: from pyopencl.tools import pytest_generate_tests_for_pyopencl \ as pytest_generate_tests in your `pytest `_ test scripts allows you to use the arguments *ctx_factory*, *device*, or *platform* in your test functions, and they will automatically be run for each OpenCL device/platform in the system, as appropriate. The following two environment variables are also supported to control device/platform choice:: PYOPENCL_TEST=0:0,1;intel=i5,i7 Device Characterization ----------------------- .. automodule:: pyopencl.characterize :members: