As a computer graphics and programming languages geek, I am delighted to have found myself working on several GPU compilers in the past two years. This began in 2021 when I started to contribute to taichi, a Python library that compiles Python functions into GPU kernels in CUDA, Metal, or Vulkan. Later on, I joined Meta and started working on SparkSL, which is the shader language that powers cross-platform GPU programming for AR effects on Instagram and Facebook. Aside from personal pleasure, I have always believed, or at least hoped, that these frameworks are actually quite useful; they make GPU programming more accessible to non-experts, empowering people to create fascinating graphics content without having to master complex GPU concepts.
In my latest installment of compilers, I turned my eyes to WebGPU -- the next-generation graphics API for the web. WebGPU promises to bring high-performance graphics via low CPU overhead and explicit GPU control, aligning with the trend started by Vulkan and D3D12 some seven years ago. Just like Vulkan, the performance benefits of WebGPU come at the cost of a steep learning curve. Although I'm confident that this won't stop talented programmers around the world from building amazing content with WebGPU, I wanted to provide people with a way to play with WebGPU without having to confront its complexity. This is how taichi.js came to be.