# Installation

Installation information is maintained collaboratively on the PyOpenCL Wiki.

# Acknowledgments

Too many to list. Please see the commit log for detailed acknowledgments.

# Tips

## Syntax highlighting

You can obtain Vim syntax highlighting for OpenCL C inlined in Python by checking this file.

Note that the triple-quoted strings containing the source must start with “”“//CL// ...”“”.

## IPython integration

PyOpenCL comes with IPython integration, which lets you seamlessly integrate PyOpenCL kernels into your IPython notebooks. Simply load the PyOpenCL IPython extension using:

    %load_ext pyopencl.ipython_ext

and then use the `%%cl_kernel` ‘cell-magic’ command. See this notebook (which ships with PyOpenCL) for a demonstration.

You can pass build options to be used for building the program executable by using the `-o` flag on the first line of the cell (next to the `%%cl_kernel` directive). For example: %%cl_kernel -o “-cl-fast-relaxed-math”`.

There are also line magics: `cl_load_edit_kernel` which will load a file into the next cell (adding `cl_kernel` to the first line) and `cl_kernel_from_file` which will compile kernels from a file (as if you copy-and-pasted the contents of the file to a cell with `cl_kernel`). Boths of these magics take options `-f` to specify the file and optionally `-o` for build options.

New in version 2014.1.

# Guidelines

## API Stability

I consider PyOpenCL’s API “stable”. That doesn’t mean it can’t change. But if it does, your code will generally continue to run. It may however start spewing warnings about things you need to change to stay compatible with future versions.

Deprecation warnings will be around for a whole year, as identified by the first number in the release name. (the “2014” in “2014.1”) I.e. a function that was deprecated in 2014.n will generally be removed in 2015.n (or perhaps later). Further, the stability promise applies for any code that’s part of a released version. It doesn’t apply to undocumented bits of the API, and it doesn’t apply to unreleased code downloaded from git.

## Relation with OpenCL’s C Bindings

We’ve tried to follow these guidelines when binding the OpenCL’s C interface to Python:

- Remove the cl_, CL_ and cl prefix from data types, macros and function names.
- Follow PEP 8, i.e.
  - Make function names lowercase.
  - If a data type or function name is composed of more than one word, separate the words with a single underscore.
- get_info functions become attributes.
- Object creation is done by constructors, to the extent possible. (i.e. minimize use of “factory functions”)
- If an operation involves two or more “complex” objects (like e.g. a kernel enqueue involves a kernel and a queue), refuse the temptation to guess which one should get a method for the operation. Instead, simply leave that command to be a function.

## Interoperability with other OpenCL software

Just about every object in `pyopencl` supports the following interface (here shown as an example for `pyopencl.MemoryObject`, from which `pyopencl.Buffer` and `pyopencl.Image` inherit):

- `pyopencl.MemoryObject.from_int_ptr()`
- `pyopencl.MemoryObject.int_ptr`

This allows retrieving the C-level pointer to an OpenCL object as a Python integer, which may then be passed to other C libraries whose interfaces expose OpenCL objects. It also allows turning C-level OpenCL objects obtained from other software to be turned into the corresponding `pyopencl` objects.

New in version 2013.2.

# User-visible Changes

## Version 2015.2

Note

This version is currently under development. You can get snapshots from PyOpenCL’s git repository

## Version 2015.1

- Support for new-style buffer protocol
- Numerous fixes

## Version 2014.1

- ipython-integration
- Bug fixes

## Version 2013.2

- Add `pyopencl.array.Array.map_to_host()`.
- Support strides on `pyopencl.enqueue_map_buffer()` and `pyopencl.enqueue_map_image()`.
- `pyopencl.ImageFormat` was made comparable and hashable.
- `pyopencl.reduction` supports slicing (contributed by Alex Nitz)
- Added Interoperability with other OpenCL software
- Bug fixes

## Version 2013.1

- Vastly improved Prefix Sums (“scan”).

- Add `pyopencl.tools.match_dtype_to_c_struct()`, for better integration of the CL and `numpy` type systems.

- More/improved Bessel functions. See the source.

- Add `PYOPENCL_NO_CACHE` environment variable to aid debugging. (e.g. with AMD’s CPU implementation, see their programming guide)

- Deprecated `pyopencl.tools.register_dtype()` in favor of `pyopencl.tools.get_or_register_dtype()`.

- Clean up the `pyopencl.array.Array` constructor interface.

- Deprecate `pyopencl.array.DefaultAllocator`.

- Deprecate `pyopencl.tools.CLAllocator`.

- Introduce `pyopencl.tools.DeferredAllocator`, `pyopencl.tools.ImmediateAllocator`.

- Allow arrays whose beginning does not coincide with the beginning of their `pyopencl.array.Array.data` `pyopencl.Buffer`. See `pyopencl.array.Array.base_data` and `pyopencl.array.Array.offset`. Note that not all functions in PyOpenCL support such arrays just yet. These will fail with `pyopencl.array.ArrayHasOffsetError`.

- Add `pyopencl.array.Array.__getitem__()` and `pyopencl.array.Array.__setitem__()`, supporting generic slicing.

  It is possible to create non-contiguous arrays using this functionality. Most operations (elementwise etc.) will not work on such arrays.

  Note also that some operations (specifically, reductions and scans) on sliced arrays that start past the beginning of the original array will fail for now. This will be fixed in a future release.

- `pyopencl.CommandQueue` may be used as a context manager (in a `with` statement)

- Add `pyopencl.clmath.atan2()`, `pyopencl.clmath.atan2pi()`.

- Add `pyopencl.array.concatenate()`.

- Add `pyopencl.Kernel.capture_call()`.

Note

The addition of `pyopencl.array.Array.__getitem__()` has an unintended consequence due to numpy bug 3375. For instance, this expression:

    numpy.float32(5) * some_pyopencl_array

may take a very long time to execute. This is because `numpy` first builds an object array of (compute-device) scalars (!) before it decides that that’s probably not such a bright idea and finally calls `pyopencl.array.Array.__rmul__()`.

Note that only left arithmetic operations of `pyopencl.array.Array` by `numpy` scalars are affected. Python’s number types (`float` etc.) are unaffected, as are right multiplications.

If a program that used to run fast suddenly runs extremely slowly, it is likely that this bug is to blame.

Here’s what you can do:

- Use Python scalars instead of `numpy` scalars.
- Switch to right multiplications if possible.
- Use a patched `numpy`. See the bug report linked above for a pull request with a fix.
- Switch to a fixed version of `numpy` when available.

## Version 2012.1

- Support for complex numbers.
- Support for Bessel functions. (experimental)
- Numerous fixes.

## Version 2011.2

- Add `pyopencl.enqueue_migrate_mem_object()`.
- Add `pyopencl.image_from_array()`.
- IMPORTANT BUGFIX: Kernel caching was broken for all the 2011.1.x releases, with severe consequences on the execution time of `pyopencl.array.Array` operations. Henrik Andresen at a PyOpenCL workshop at DTU first noticed the strange timings.
- All comparable PyOpenCL objects are now also hashable.
- Add `pyopencl.tools.context_dependent_memoize()` to the documented functionality.
- Base `pyopencl.clrandom` on RANLUXCL, add functionality.
- Add `pyopencl.NannyEvent` objects.
- Add `pyopencl.characterize`.
- Ensure compatibility with OS X Lion.
- Add `pyopencl.tools.register_dtype()` to enable scan/reduction on struct types.
- `pyopencl.enqueue_migrate_mem_object()` was renamed `pyopencl.enqueue_migrate_mem_object_ext()`. `pyopencl.enqueue_migrate_mem_object()` now refers to the OpenCL 1.2 function of this name, if available.
- `pyopencl.create_sub_devices()` was renamed `pyopencl.create_sub_devices_ext()`. `pyopencl.create_sub_devices()` now refers to the OpenCL 1.2 function of this name, if available.
- Alpha support for OpenCL 1.2.

## Version 2011.1.2

- More bug fixes.

## Version 2011.1.1

- Fixes for Python 3 compatibility. (with work by Christoph Gohlke)

## Version 2011.1

- All is_blocking parameters now default to True to avoid crashy-by-default behavior. (suggested by Jan Meinke) In particular, this change affects `pyopencl.enqueue_read_buffer()`, `pyopencl.enqueue_write_buffer()`, `pyopencl.enqueue_read_buffer_rect()`, `pyopencl.enqueue_write_buffer_rect()`, `pyopencl.enqueue_read_image()`, `pyopencl.enqueue_write_image()`, `pyopencl.enqueue_map_buffer()`, `pyopencl.enqueue_map_image()`.
- Add `pyopencl.reduction`.
- Add Reductions.
- Add `pyopencl.scan`.
- Add `pyopencl.MemoryObject.get_host_array()`.
- Deprecate context arguments of `pyopencl.array.to_device()`, `pyopencl.array.zeros()`, `pyopencl.array.arange()`.
- Make construction of `pyopencl.array.Array` more flexible (cqa argument.)
- Add Memory Pools.
- Add vector types, see `pyopencl.array.vec`.
- Add `pyopencl.array.Array.strides`, `pyopencl.array.Array.flags`. Allow the creation of arrys in C and Fortran order.
- Add `pyopencl.enqueue_copy()`. Deprecate all other transfer functions.
- Add support for numerous extensions, among them device fission.
- Add a compiler cache.
- Add the ‘g_times_l’ keyword arg to kernel execution.

## Version 0.92

- Add support for OpenCL 1.1.
- Add support for the cl_khr_gl_sharing extension, leading to working GL interoperability.
- Add `pyopencl.Kernel.set_args()`.
- The call signature of `pyopencl.Kernel.__call__()` changed to emphasize the importance of local_size.
- Add `pyopencl.Kernel.set_scalar_arg_dtypes()`.
- Add support for the cl_nv_device_attribute_query extension.
- Add `pyopencl.array.Array()` and related functionality.
- Make build not depend on Boost C++.

## Version 0.91.5

- Add `pyopencl.ImageFormat.channel_count`, `pyopencl.ImageFormat.dtype_size`, `pyopencl.ImageFormat.itemsize`.
- Add missing `pyopencl.enqueue_copy_buffer()`.
- Add `pyopencl.create_some_context()`.
- Add `pyopencl.enqueue_barrier()`, which was previously missing.

## Version 0.91.4

A bugfix release. No user-visible changes.

## Version 0.91.3

- All parameters named host_buffer were renamed hostbuf for consistency with the `pyopencl.Buffer` constructor introduced in 0.91. Compatibility code is in place.
- The `pyopencl.Image` constructor does not need a shape parameter if the given hostbuf has hostbuf.shape.
- The `pyopencl.Context` constructor can now be called without parameters.

## Version 0.91.2

- `pyopencl.Program.build()` now captures build logs and adds them to the exception text.
- Deprecate `pyopencl.create_context_from_type()` in favor of second form of `pyopencl.Context` constructor
- Introduce `pyopencl.LocalMemory`.
- Document kernel invocation and `pyopencl.Kernel.set_arg()`.

## Version 0.91.1

- Fixed a number of bugs, notably involving `pyopencl.Sampler`.
- `pyopencl.Device`, `pyopencl.Platform`, `pyopencl.Context` now have nicer string representations.
- Add `Image.shape`. (suggested by David Garcia)

## Version 0.91

- Add GL Interoperability.
- Add a test suite.
- Fix numerous get_info bugs. (reports by David Garcia and the test suite)
- Add `pyopencl.ImageFormat.__repr__()`.
- Add `pyopencl.addressing_mode.to_string()` and colleagues.
- The pitch arguments to `pyopencl.create_image_2d()`, `pyopencl.create_image_3d()`, `pyopencl.enqueue_read_image()`, and `pyopencl.enqueue_write_image()` are now defaulted to zero. The argument order of enqueue_{read,write}_image has changed for this reason.
- Deprecate `pyopencl.create_image_2d()`, `pyopencl.create_image_3d()` in favor of the `pyopencl.Image` constructor.
- Deprecate `pyopencl.create_program_with_source()`, `pyopencl.create_program_with_binary()` in favor of the `pyopencl.Program` constructor.
- Deprecate `pyopencl.create_buffer()`, `pyopencl.create_host_buffer()` in favor of the `pyopencl.Buffer` constructor.
- `pyopencl.MemoryObject.get_image_info()` now actually exists.
- Add `pyopencl.MemoryObject.image.info`.
- Fix API tracing.
- Add constructor arguments to `pyopencl.ImageFormat`. (suggested by David Garcia)

## Version 0.90.4

- Add build fixes for Windows and OS X.

## Version 0.90.3

- Fix a GNU-ism in the C++ code of the wrapper.

## Version 0.90.2

- Fix `pyopencl.Platform.get_info()`.
- Fix passing properties to `pyopencl.CommandQueue`. Also fix related documentation.

## Version 0.90.1

- Fix building on the Mac.

## Version 0.90

- Initial release.

# License

PyOpenCL is licensed to you under the MIT/X Consortium license:

Copyright (c) 2009-13 Andreas Klöckner and Contributors.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

PyOpenCL includes derivatives of parts of the Thrust computing package (in particular the scan implementation). These parts are licensed as follows:

> Copyright 2008-2011 NVIDIA Corporation

> Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

> > <http://www.apache.org/licenses/LICENSE-2.0>

> Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Note

If you use Apache-licensed parts, be aware that these may be incompatible with software licensed exclusively under GPL2. (Most software is licensed as GPL2 or later, in which case this is not an issue.)

PyOpenCL includes the RANLUXCL random number generator:

> Copyright (c) 2011 Ivar Ursin Nikolaisen

> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

> The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

> THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

# Frequently Asked Questions

The FAQ is maintained collaboratively on the Wiki FAQ page.

# Citing PyOpenCL

We are not asking you to gratuitously cite PyOpenCL in work that is otherwise unrelated to software. That said, if you do discuss some of the development aspects of your code and would like to highlight a few of the ideas behind PyOpenCL, feel free to cite this article:

> Andreas Klöckner, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, Ahmed Fasih, PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation, Parallel Computing, Volume 38, Issue 3, March 2012, Pages 157-174.

Here’s a Bibtex entry for your convenience:

    @article{kloeckner_pycuda_2012,
       author = {{Kl{\"o}ckner}, Andreas
            and {Pinto}, Nicolas
            and {Lee}, Yunsup
            and {Catanzaro}, B.
            and {Ivanov}, Paul
            and {Fasih}, Ahmed },
       title = "{PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation}",
       journal = "Parallel Computing",
       volume = "38",
       number = "3",
       pages = "157--174",
       year = "2012",
       issn = "0167-8191",
       doi = "10.1016/j.parco.2011.09.001",
    }