GPU architecture resources

I am often get asked in DMs about how GPUs work. There is a lot of information on GPU architectures online, one can start with these:

• Render Hell 2.0: Easy to follow and thorough introduction with extensive list of references for further study.
• Life of a triangle – NVIDIA’s logical pipeline. High level view of the NVidia’s GPU architecture
• Triangles are precious. High level view of AMD’s GPU (GCN) architecture.

And then can refer to these for a more in-depth study:

• From Shader Code to a Teraflop: How GPU Shader Cores Work, Kayvon Fatahalian’s seminal presentation on GPU architectures. It is also worth check out his Parallel Computer Architecture and Programming Stanford course.
• Trip down the GPU lane with Machine Learning, this presentation has a machine learning twist but also a good introduction to GPU architecture
• A trip through the Graphics Pipeline, comprehensive series of posts on how each GPU pipeline stage works.
• Understanding Modern GPUs, another series of blogposts on how the various GPU pipeline components work.
• Low-Level GPU Documentation, a large collection of publicly available GPU documentation including NVidia, AMD and Intel
• AMD GPU Open good resource, they often share low-level posts on AMD GPUs.
• Intel processor graphics: architecture and programming, low level presentation with a lot of details on Intel’s GPU architecture.

It is also a good idea to look for documentation about how compute shaders work, like:

• Introduction to compute shaders, More compute shaders and Even more compute shaders, a great series on compute shaders.
• Compute Shaders: Optimize your engine using compute, introduction to compute shaders and how do they differ from vertex and pixel shaders from a programming and a hardware perspective.
• Intro to parallel programming, worth watching course on programming with compute shaders (CUDA, but transferable).

as they are “closer to the metal” than pixel/vertex shaders in that they require you to think in threads and often synchronise execution and manage memory.

Also posts that discuss GPU profiling and performance, such as

• The Peak-Performance-Percentage Analysis Method for Optimizing Any GPU Workload and also Fixing the Hyperdrive: Maximizing Rendering Performance on NVIDIA GPUs.
• GPU Performance for Game Artists
• What’s up with my branch on GPU?

as they often expose details about how a GPU works.

Additionally, it is worth searching for “GPU” or “performance” in the GDC Vault, it can often return some relevant presentations.

I am closing this nowhere near exhaustive list with a few alternative presentations of GPU architecture concepts:

• Understanding the anatomy of GPUs using Pokémon, which uses a trading card paradigm for GPU architecture learning
• Where do GPUs come from, which uses a robot manufacturing pipeline paradigm to explain how GPUs work.