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.
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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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Hardcover. Condición: new. Hardcover. The WebGPU Sourcebook: High-Performance Graphics and Machine Learning in the Browser explains how to code web applications that access the clients 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 theyre used in practiceExplains the theory of graphical rendering and shows how to implement rendering inside a web applicationExamines the theory of neural networks (machine learning) and shows how to create a web application that trains and executes a neural network This explains how to code web applications that use WebGPU to access the clients 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781032728407
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