tag: gpu

Introducing Galry, a high-performance interactive 2D visualization Python package


I'm releasing today the code of a first experimental version of Galry, a high-performance interactive 2D visualization Python package that I'm creating as part of my current research project.

A tutorial on OpenGL/OpenCL interoperability in Python


In the last two posts, I've shown how to use OpenCL for GPGPU, and OpenGL for graphics rendering, with Python. Here I'll show how both OpenCL and OpenGL can be used at the same time with Python. It's called OpenCL-OpenGL interoperability. What is it about?

2D graphics rendering tutorial with PyOpenGL


UPDATE: you may be interested in the Vispy library, which provides easier and more Pythonic access to OpenGL.

OpenGL is a widely used open and cross-platform library for real-time 3D graphics, developed more than twenty years ago. It provides a low-level API that allows the developer to access the graphics hardware in an uniform way. It is the platform of choice when developing complex 2D or 3D applications that require hardware acceleration and that need to work on different platforms. It can be used in a number of languages including C/C++, C#, Java, Objective-C (used in iPhone and iPad games), Python, etc. In this article, I'll show how OpenGL can be used with Python (thanks to the PyOpenGL library) to efficiently render 2D graphics.

A PyOpenCL tutorial on Windows with or without a GPU


I've been using CUDA and PyCUDA as GPGPU platforms for a few years now. They enable access to the incredible computational power of graphics cards through a simple C-like language. A recent Nvidia graphics card is nevertheless required in order to execute CUDA code. Some computers may not include a Nvidia GPU, but rather an AMD/ATI card or even an integrated graphics processor. Those computers thus cannot execute a CUDA program.