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Master Metal: The Next-Generation Graphics and GPU Programming Platform for Apple Developers
Metal enables Apple developers to maximize performance in demanding tasks like 3D graphics, games, scientific programming, visualization, and GPU-accelerated machine learning. Metal® Programming Guide is the authoritative, practical guide to Metal for all iOS programmers who are interested in graphics programming but don’t know where to start.
Pioneering Apple developer Janie Clayton covers everything from basic draw calls to advanced parallel computing, combining easy-to-understand conceptual explanations with well-tested Swift 4/Xcode 9 sample code (available for download at GitHub).
Clayton introduces the essential Metal, graphics, and math concepts every graphics programmer needs to know. She also discusses key graphics-specific libraries, concepts, and Metal Classes, presenting techniques and examples you’ll find valuable for both graphics and data processing. Clayton also provides coverage of the Metal Compute Pipeline, demonstrating practical GPU programming applications ranging from image processing to neural networking.
Preface
Acknowledgments
About the Author
Part I: Metal Basics
Chapter 1: What Is Metal?
Chapter 2: Overview of Rendering and Raster Graphics
Chapter 3: Your First Metal Application (Hello, Triangle!)
Part II: Rendering and Graphics
Chapter 4: Essential Mathematics for Graphics
Chapter 5: Intro to Shaders
Chapter 6: Metal Resources and Memory Management
Chapter 7: Libraries, Functions, and Pipeline States
Chapter 8: 2D Drawing
Chapter 9: Introduction to 3D Drawing
Chapter 10: Advanced 3D Drawing
Chapter 11: Interfacing with Model I/O
Chapter 12: Texturing and Sampling
Chapter 13: Multi-Pass Rendering Techniques
Chapter 14: Geometry Unleashed: Tessellation in Metal
Part III: Data Parallel Programming
Chapter 15: The Metal Compute Pipeline
Chapter 16: Image Processing in Metal
Chapter 17: Machine Vision
Chapter 18: Metal Performance Shaders Framework
Chapter 19: Neural Network Concepts
Chapter 20: Convolutional Neural Networks