python-cuda | Python bindings for CUDA 2.1 with numpy integration | GPU library

by npinto Python Version: Current License: Non-SPDX

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kandi X-RAY | python-cuda Summary

python-cuda is a Python library typically used in Hardware, GPU, Numpy applications. python-cuda has no bugs, it has no vulnerabilities, it has build file available and it has low support. However python-cuda has a Non-SPDX License. You can download it from GitHub.

Python bindings for CUDA 2.1 with numpy integration

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Support

python-cuda has a low active ecosystem.

It has 25 star(s) with 3 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of python-cuda is current.

Quality

python-cuda has no bugs reported.

Security

python-cuda has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

python-cuda has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

python-cuda releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed python-cuda and discovered the below as its top functions. This is intended to give you an instant insight into python-cuda implemented functionality, and help decide if they suit your requirements.

FFT - convolve a 2D numpy array .
convert object to numpy array
Entry point for the script .
Compile a source code file .
Compile a Python source code .
Download setuptools .
Get device properties .
Use setuptools .
Get information about a function .
removes only the headers from the specified file

Get all kandi verified functions for this library.

python-cuda Key Features

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python-cuda Examples and Code Snippets

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Community Discussions

Trending Discussions on python-cuda

Cuda GPU is slower than CPU in simple numpy operation

QUESTION

Cuda GPU is slower than CPU in simple numpy operation

Asked 2017-Dec-10 at 08:51

I am using this code based on this article to see the GPU accelerations, but all I can see is slowdown:

...

ANSWER

Answered 2017-Dec-08 at 09:11

Probably your array is too small and the operation too simple to offset the cost of data transfer associated to the GPU. Other way to see it, is that you're not being fair in your timing since for the GPU it also is timing the memory transfer time and not only the processing time.

Try some more challenging example, maybe first an element wise big matrix multiplication and then a matrix multiplication.

In the end, the power of the GPU is to perform many operations on the same data so you end up paying only once the data transfer cost.

Source https://stackoverflow.com/questions/47710707

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install python-cuda

You can download it from GitHub.
You can use python-cuda like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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