Scarpino | The WebGPU Sourcebook | Buch | 978-1-032-72667-0 | www.sack.de

Buch, Englisch, 384 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 558 g

Scarpino

The WebGPU Sourcebook

High-Performance Graphics and Machine Learning in the Browser
1. Auflage 2024
ISBN: 978-1-032-72667-0
Verlag: CRC Press

High-Performance Graphics and Machine Learning in the Browser

Buch, Englisch, 384 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 558 g

ISBN: 978-1-032-72667-0
Verlag: CRC Press


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

Scarpino The WebGPU Sourcebook jetzt bestellen!

Zielgruppe


Professional Practice & Development, Professional Reference, and Professional Training


Autoren/Hrsg.


Weitere Infos & Material


1. Introduction, 2. Fundamental Objects, 3. Rendering Graphics, 4. The WebGPU Shading Language (WGSL), 5. Uniforms and Transformations, 6. Lighting, Textures, and Depth, 7. Advanced Features, 8. Compute Applications, 9. Machine Learning with Neural Networks, 10. Image and Video Processing, 11. Matrix Operations, 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 Master’s in Electrical Engineering in 2002, and has been a professional programmer and author ever since.



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