Book Image

Mastering Graphics Programming with Vulkan

By : Marco Castorina, Gabriel Sassone
5 (1)
Book Image

Mastering Graphics Programming with Vulkan

5 (1)
By: Marco Castorina, Gabriel Sassone

Overview of this book

Vulkan is now an established and flexible multi-platform graphics API. It has been adopted in many industries, including game development, medical imaging, movie productions, and media playback. Learning Vulkan is a foundational step to understanding how a modern graphics API works, both on desktop and mobile. In Mastering Graphics Programming with Vulkan, you’ll begin by developing the foundations of a rendering framework. You’ll learn how to leverage advanced Vulkan features to write a modern rendering engine. The chapters will cover how to automate resource binding and dependencies. You’ll then take advantage of GPU-driven rendering to scale the size of your scenes and finally, you’ll get familiar with ray tracing techniques that will improve the visual quality of your rendered image. By the end of this book, you’ll have a thorough understanding of the inner workings of a modern rendering engine and the graphics techniques employed to achieve state-of-the-art results. The framework developed in this book will be the starting point for all your future experiments.
Table of Contents (21 chapters)
1
Part 1: Foundations of a Modern Rendering Engine
7
Part 2: GPU-Driven Rendering
13
Part 3: Advanced Rendering Techniques

Implementing cloth simulation using async compute

In this section, we are going to implement a simple cloth simulation on the GPU as an example use case of a compute workload. We start by explaining why running some tasks on the GPU might be beneficial. Next, we provide an overview of compute shaders. Finally, we show how to port code from the CPU to the GPU and highlight some of the differences between the two platforms.

Benefits of using compute shaders

In the past, physics simulations mainly ran on the CPU. GPUs only had enough compute capacity for graphics work, and most stages in the pipeline were implemented by dedicated hardware blocks that could only perform one task. As GPUs evolved, pipeline stages moved to generic compute blocks that could perform different tasks.

This increase both in flexibility and compute capacity has allowed engine developers to move some workloads on the GPU. Aside from raw performance, running some computations on the GPU avoids expensive...