cffi VS PyCUDA

Compare cffi vs PyCUDA and see what are their differences.

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cffi PyCUDA
- -
- 1,740
- -
- 5.4
almost 8 years ago 16 days ago
Python
- GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

cffi

Posts with mentions or reviews of cffi. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning cffi yet.
Tracking mentions began in Dec 2020.

PyCUDA

Posts with mentions or reviews of PyCUDA. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning PyCUDA yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing cffi and PyCUDA you can also consider the following projects:

SWIG - SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages.

jpype - JPype is cross language bridge to allow Python programs full access to Java class libraries.

PyJNIus - Access Java classes from Python

pyopencl - OpenCL integration for Python, plus shiny features

scikit-cuda - Python interface to GPU-powered libraries

TileDB-Py - Python interface to the TileDB storage engine

aicsimageio - Image Reading, Metadata Conversion, and Image Writing for Microscopy Images in Python

entangle - A lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs.

tmu - Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.