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::
    In version 2013.1, :class:`CLAllocator` was deprecated and replaced
    by :class:`DeferredAllocator`.
.. method:: __call__(size)
    Allocate a :class:`pyopencl.Buffer` of the given *size*.

.. 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*.

.. class:: MemoryPool(allocator)

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.
.. 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 <http://pytest.org>`_ 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: