pycuda 2013.1.1+git20140310-1ubuntu1 source package in Ubuntu
Changelog
pycuda (2013.1.1+git20140310-1ubuntu1) trusty; urgency=medium * Merge with Debian unstable. (LP: #1289463) * d/control: - Add alternate build-depends on libcuda-5.5-1 pycuda (2013.1.1+git20140310-1) unstable; urgency=low * New upstream version. * Change python-pycuda-doc recommendation of Python and Python 3 packages to suggestions to avoid installing unwanted packages, like in #739173. * Rebuild with Python 3.4 support. * Update Standards-Version to 3.9.5; no changes necessary. pycuda (2013.1.1+git20131128-1) unstable; urgency=low * New upstream version. * Rebuild against CUDA 5.5 (Closes: #730263). pycuda (2013.1.1-1) unstable; urgency=low * New upstream release. -- Graham Inggs <email address hidden> Sun, 13 Apr 2014 09:27:10 +0200
Upload details
- Uploaded by:
- Graham Inggs
- Uploaded to:
- Trusty
- Original maintainer:
- Ubuntu Developers
- Architectures:
- amd64 i386 all
- Section:
- python
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Trusty | release | multiverse | python |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| pycuda_2013.1.1+git20140310.orig.tar.gz | 215.6 KiB | 1108968c12024a93fda97b4433e09f13f1110fdb257b0d2575b24330c3339400 |
| pycuda_2013.1.1+git20140310-1ubuntu1.debian.tar.gz | 9.1 KiB | db9f39493ac66391f60c7554e5e408a200028841cb89e4c82ab1a5d050c1a4a2 |
| pycuda_2013.1.1+git20140310-1ubuntu1.dsc | 2.6 KiB | a6b6e1c5f3aa052b308219d2fb6775600bd13668379a5d0813c17a8f738f7b86 |
Available diffs
Binary packages built by this source
- python-pycuda: Python module to access Nvidia‘s CUDA parallel computation API
PyCUDA lets you access Nvidia‘s 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 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.
* 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.
- python-pycuda-dbg: Python module to access Nvidia‘s CUDA API (debug extensions)
PyCUDA lets you access Nvidia‘s 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 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.
* 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.
.
This package contains debug extensions build for the Python debug interpreter.
- python-pycuda-doc: module to access Nvidia‘s CUDA computation API (documentation)
PyCUDA lets you access Nvidia‘s 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 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.
* 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.
.
This package contains HTML documentation and example scripts.
- python3-pycuda: No summary available for python3-pycuda in ubuntu utopic.
No description available for python3-pycuda in ubuntu utopic.
- python3-pycuda-dbg: Python 3 module to access Nvidia‘s CUDA API (debug extensions)
PyCUDA lets you access Nvidia‘s 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 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.
* 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.
.
This package contains debug extensions for the Python 3 debug interpreter.
