The WebGPU Sourcebook
eBook - ePub

The WebGPU Sourcebook

High-Performance Graphics and Machine Learning in the Browser

  1. 384 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

The WebGPU Sourcebook

High-Performance Graphics and Machine Learning in the Browser

About this book

The WebGPU Sourcebook: High-Performance Graphics and Machine Learning in the Browser explains how to code web applications that access the client's graphics processor unit, or GPU. This makes it possible to render graphics in a browser at high speed and perform computationally intensive tasks such as machine learning. By taking advantage of WebGPU, web developers can harness the same performance available to desktop developers.

The first part of the book introduces WebGPU at a high level, without graphics theory or heavy math. The chapters in the second part are focused on graphical rendering and the rest of the book focuses on compute shaders.

This book walks through several examples of WebGPU usage. It also:

  • Discusses the classes and functions defined in the WebGPU API and shows how they're used in practice
  • Explains the theory of graphical rendering and shows how to implement rendering inside a web application
  • Examines the theory of neural networks (machine learning) and shows how to create a web application that trains and executes a neural network

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Yes, you can access The WebGPU Sourcebook by Matthew Scarpino in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Graphics. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Contents
  6. Preface
  7. 1 Introduction
  8. 2 Fundamental Objects
  9. 3 Rendering Graphics
  10. 4 The WebGPU Shading Language (WGSL)
  11. 5 Uniforms and Transformations
  12. 6 Lighting, Textures, and Depth
  13. 7 Advanced Features
  14. 8 Compute Applications
  15. 9 Machine Learning with Neural Networks
  16. 10 Image and Video Processing
  17. 11 Matrix Operations
  18. 12 Filtering Audio with the Fast Fourier Transform (FFT)
  19. Appendix A: Node and TypeScript
  20. Appendix B: WebAssembly, Emscripten, and Google Dawn
  21. Index