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  • Learn how to install GLEW on Windows and build your programs to use it
  • Learn how to employ OpenGL extensions in your program
  • Explore all the functions and capabilities of GLEW
  • Learn about applications that make use of OpenGL extensions interesting

3D graphics hardware is evolving quickly and expanding across devices ranging from smartphones to tablets to computers. OpenGL Extensions help vendors to expose the cutting-edge features of their hardware to developers in a usable manner. However, the mix of different hardware and operating system versions can make the use of these extensions quite difficult. The solution to this is the OpenGL Extension Wrangler (GLEW) library.

"Instant GLEW" is a quick guide to learn how to install GLEW on Windows and how to use it in your OpenGL programs. You will learn how to use any OpenGL Extension you want in your program in a safe and portable manner.

"Instant GLEW" explains how OpenGL Extensions are used to expose cutting-edge features through the OpenGL API. You will learn how to install GLEW on Windows and configure your OpenGL program to build with it. The book starts with an introduction to GLEW and details the benefits of its usage in OpenGL programs.

Later, the book guides you through configuring Visual Studio or a Linux compile environment for using GLEW. This book comprises of short codes that illustrate simple common extensions. You will also find out about alternatives to GLEW and how to use extensions without GLEW.

  • Learn something new in an Instant! A short, fast, focused guide delivering immediate results
  • Learn about the usages of GLEW and how to use OpenGL extensions in your programs
  • Learn about the functionalities of GLEW
  • Discover utilities to use OpenGL extensions
  • You can find the updated code here.
Page Count 42
Course Length 1 hours 15 minutes
ISBN 9781783280483
Date Of Publication 25 Jul 2013


Ashwin Nanjappa

Ashwin Nanjappa is a senior architect at NVIDIA, working in the TensorRT team on improving deep learning inference on GPU accelerators. He has a PhD from the National University of Singapore in developing GPU algorithms for the fundamental computational geometry problem of 3D Delaunay triangulation. As a post-doctoral research fellow at the BioInformatics Institute (Singapore), he developed GPU-accelerated machine learning algorithms for pose estimation using depth cameras. As an algorithms research engineer at Visenze (Singapore), he implemented computer vision algorithm pipelines in C++, developed a training framework built upon Caffe in Python, and trained deep learning models for some of the world's most popular online shopping portals.