Result for 1FBAD0DC0B75156318A062A7BD93E8D66CFA6EBF

Query result

Key Value
FileName./usr/lib/python2.7/dist-packages/pycuda/compyte/dtypes.py
FileSize7855
MD53C9781FE914F5F5DA13E43262AD3FEB9
SHA-11FBAD0DC0B75156318A062A7BD93E8D66CFA6EBF
SHA-256BAB6A03D030F697EC8895688C3F70AFB4B33CAFBF9296B9BC298D7E3A2DC3622
SSDEEP192:jQH5nxOCkzRJAoxF7MEShX8+Pop0ivxZkqODZLACfn/pA7bsA9aVbssf:jcU7/GMVwrJjiM
TLSHT122F1964B560098239793843F4E66F482A317BB8F658438747AECB1B96F52328C3F579C
hashlookup:parent-total4
hashlookup:trust70

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Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize290830
MD5ECD3EFCFE4F75515834707D724A8BE41
PackageDescriptionPython 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.
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython-pycuda
PackageSectioncontrib/python
PackageVersion2014.1-3
SHA-104E0D8145D3810F0F28BAB86ADB02E7BFD9A6F8B
SHA-256FD0D0950BAE13D67BB17FE667E05609102B44E4B21B78C42D964C0DF4956F9D6
Key Value
FileSize304682
MD5CE0BA381CB2F51A9F332F8D8E8767F8C
PackageDescriptionPython 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.
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython-pycuda
PackageSectioncontrib/python
PackageVersion2014.1-3
SHA-18951AABF3818022D4EC45C4A1662A43B5ED755EE
SHA-256CA16EB9ADB14EA9379C1705FD381C3E9388031223C58F40F54B8AF67F5678330
Key Value
FileSize350082
MD5F79612E0871F9237EA1D5D8D8A3EF646
PackageDescriptionPython module to access OpenCL parallel computation API PyOpenCL lets you access the OpenCL parallel computation API from Python. Here's what sets PyOpenCL apart: * 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. * Completeness. PyOpenCL puts the full power of OpenCL’s API at your disposal, if you wish. * Convenience. While PyOpenCL's primary focus is to make all of OpenCL accessible, it tries hard to make your life less complicated as it does so--without taking any shortcuts. * Automatic Error Checking. All OpenCL errors are automatically translated into Python exceptions. * Speed. PyOpenCL’s base layer is written in C++, so all the niceties above are virtually free. * Helpful, complete documentation and a wiki. * Liberal licensing (MIT).
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython-pyopencl
PackageSectionpython
PackageVersion2014.1-3
SHA-15D93DA0DE26A6DF52D362C857411C6B3285C5F92
SHA-25660BDB0E2982B0A816ED42742833F34255E28B9082561318B5BA5EDA622F88AD6
Key Value
FileSize355648
MD5B057B3961B4F85FA32CB35504D31B302
PackageDescriptionPython module to access OpenCL parallel computation API PyOpenCL lets you access the OpenCL parallel computation API from Python. Here's what sets PyOpenCL apart: * 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. * Completeness. PyOpenCL puts the full power of OpenCL’s API at your disposal, if you wish. * Convenience. While PyOpenCL's primary focus is to make all of OpenCL accessible, it tries hard to make your life less complicated as it does so--without taking any shortcuts. * Automatic Error Checking. All OpenCL errors are automatically translated into Python exceptions. * Speed. PyOpenCL’s base layer is written in C++, so all the niceties above are virtually free. * Helpful, complete documentation and a wiki. * Liberal licensing (MIT).
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython-pyopencl
PackageSectionpython
PackageVersion2014.1-3
SHA-1AA5265D39E371911FCCC63E965B2C339BF3CE0F9
SHA-256E208BFE9AF25E90F69FD4C0D01536D6F08111CE37E69219F7BF4C271A9F64DC2