PyCUDA lets you access `Nvidia <http://nvidia.com>`_'s `CUDA
<http://nvidia.com/cuda/>`_ parallel computation API from Python.
Several wrappers of the CUDA API already exist-so what's so special
about PyCUDA?
* Object cleanup tied to lifetime of objects. This idiom, often
called
`RAII <http://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>`_
in C++, makes it much easier to write correct, leak- and
crash-free code. PyCUDA knows about dependencies, too, so (for
example) it won't detach from a context before all memory
allocated in it is also freed.
* Convenience. Abstractions like pycuda.driver.SourceModule and
pycuda.gpuarray.GPUArray make CUDA programming even more
convenient than with Nvidia's C-based runtime.
* Completeness. PyCUDA puts the full power of CUDA's driver API at
your disposal, if you wish. It also includes code for
interoperability with OpenGL.
* Automatic Error Checking. All CUDA errors are automatically
translated into Python exceptions.
* Speed. PyCUDA's base layer is written in C++, so all the niceties
above are virtually free.
* Helpful `Documentation <http://documen.tician.de/pycuda>`_ and a
`Wiki <http://wiki.tiker.net/PyCuda>`_.
Relatedly, like-minded computing goodness for `OpenCL <http://khronos.org>`_
is provided by PyCUDA's sister project `PyOpenCL <http://pypi.python.org/pypi/pyopencl>`_.
<http://nvidia.com/cuda/>`_ parallel computation API from Python.
Several wrappers of the CUDA API already exist-so what's so special
about PyCUDA?
* Object cleanup tied to lifetime of objects. This idiom, often
called
`RAII <http://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>`_
in C++, makes it much easier to write correct, leak- and
crash-free code. PyCUDA knows about dependencies, too, so (for
example) it won't detach from a context before all memory
allocated in it is also freed.
* Convenience. Abstractions like pycuda.driver.SourceModule and
pycuda.gpuarray.GPUArray make CUDA programming even more
convenient than with Nvidia's C-based runtime.
* Completeness. PyCUDA puts the full power of CUDA's driver API at
your disposal, if you wish. It also includes code for
interoperability with OpenGL.
* Automatic Error Checking. All CUDA errors are automatically
translated into Python exceptions.
* Speed. PyCUDA's base layer is written in C++, so all the niceties
above are virtually free.
* Helpful `Documentation <http://documen.tician.de/pycuda>`_ and a
`Wiki <http://wiki.tiker.net/PyCuda>`_.
Relatedly, like-minded computing goodness for `OpenCL <http://khronos.org>`_
is provided by PyCUDA's sister project `PyOpenCL <http://pypi.python.org/pypi/pyopencl>`_.
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pycuda-2011.1.tar.gz
(1.3 MB
view details)
File details
Details for the file pycuda-2011.1.tar.gz.
File metadata
- Download URL: pycuda-2011.1.tar.gz
- Upload date:
- Size: 1.3 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f942e8dc84a21146fca666775a40f52c1eb459dcc8620809ee1e257177313397
|
|
| MD5 |
62ff464dc82eb730dfdfdc4ab3b1f656
|
|
| BLAKE2b-256 |
dca7c14fd73e40063aec30962e30ce795a9e630485f6a36a9cdbe5ccf46dee5b
|
