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Data Structures and Algorithms: Parallel Structures, GPU Computing, and Visual Rendering with WebGPU and WGSL — cover

Book cover from the publisher catalogue.

Computer Science

Data Structures and Algorithms: Parallel Structures, GPU Computing, and Visual Rendering with WebGPU and WGSL

By Kenwright

A modern take on classic computing principles, this book explores how data structures and algorithms evolve in the age of parallelism and GPU acceleration.

Pages
746
Publication
5 October 2025
Language
English
ISBN
9798272012067
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The appeal of “Data Structures and Algorithms: Parallel Structures, GPU Computing, and Visual Rendering with WebGPU and WGSL” starts with its focus on Parallel Programming. That is fertile territory for a reader ready to move beyond collecting ideas and start making connections. WebGPU connects browser-based graphics and computation with highly visible possibilities. Choose a scene, data display, or small parallel task and use it to organise the concepts you want to explore. Keep a person and a situation in view as you explore. Ask who would use the idea, what they would need to understand, and how their experience could change your interpretation. That human question is a productive creative starting point. A promising addition to a reading list with a purpose.

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Explore Computer Science, Algorithms, Data Structures with a reading approach that gives the ideas somewhere to go. WebGPU connects browser-based graphics and computation with highly visible possibilities. Choose a scene, data display, or small parallel task and use it to organise the concepts you want to explore.

The listed 746 pages give a sense of extent; page count alone does not establish depth or difficulty.

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Publisher’s synopsis

As computing shifts toward parallel architectures and GPU acceleration, traditional approaches to data structures and algorithms must evolve. This book dives into the challenges and opportunities of modern computing, introducing readers to WebGPU and WGSL as tools for high-performance rendering and computation. Learn how to adapt classic structures like trees, graphs, and queues for parallel execution, and explore how algorithms behave in massively concurrent environments. Ideal for developers, researchers, and students aiming to stay ahead in the computing landscape.

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