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E-raamat: WebGPU Sourcebook: High-Performance Graphics and Machine Learning in the Browser [Taylor & Francis e-raamat]

  • Formaat: 374 pages, 101 Tables, black and white; 69 Line drawings, black and white; 7 Halftones, black and white; 76 Illustrations, black and white
  • Ilmumisaeg: 02-Oct-2024
  • Kirjastus: CRC Press
  • ISBN-13: 9781003422839
  • Taylor & Francis e-raamat
  • Hind: 184,65 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 263,78 €
  • Säästad 30%
  • Formaat: 374 pages, 101 Tables, black and white; 69 Line drawings, black and white; 7 Halftones, black and white; 76 Illustrations, black and white
  • Ilmumisaeg: 02-Oct-2024
  • Kirjastus: CRC Press
  • ISBN-13: 9781003422839

This explains how to code web applications that use WebGPU to access the client’s graphics processing unit (GPU). This makes it possible to render graphics at high-speed in a browser and perform computationally-intensive tasks such as machine learning.



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.

Chapter 01 Introduction
Chapter 02 Fundamental Objects
Chapter 03 Rendering Graphics
Chapter 04 The WebGPU Shading Language (WGSL)
Chapter 05 Uniforms and Transformations
Chapter 06 Lighting, Textures, and Depth
Chapter 07 Advanced Features
Chapter 08 Compute Applications
Chapter 09 Machine Learning with Neural Networks
Chapter 10 Image and Video Processing
Chapter 11 Matrix Operations
Chapter 12 Filtering Audio with the Fast Fourier Transform (FFT) Appendix A Node and TypeScript Appendix B WebAssembly, Emscripten, and Google Dawn

Matthew Scarpino is a software developer at Purdue University. He has worked on many different types of programming projects, including web applications, graphical rendering, and high-performance computing. He received his Masters in Electrical Engineering in 2002, and has been a professional programmer and author ever since.