From molecular simulation to black hole rendering - Taichi Lang makes life easier for digital content creators
It has been more than three years since I started working on a brand new programming language, Taichi-Lang, which is embedded in Python (but can perfectly run independently of Python) and designed for high-performance numerical computation. Two months ago, Taichi 1.0 was released, which is indeed a milestone for me personally and for our entire community. From an immature academic idea to an open-source project that has attracted hundreds of contributors, Taichi is committed to making graphics programming easier for content creators.
Training a magic fountain using Taichi's autodiff, an efficient tool for differentiable physical simulation
With the generated gradient information, a differentiable physical simulator can make the convergence of the machine learning process one order of magnitude faster than gradient-free algorithms, such as model-free reinforcement learning.
As you may already know, ETH Zürich is a world-class university constantly ranked among the top 1 to 5 in Europe.
On a Sunday afternoon about a couple of months ago, when Ye and I were on our way back from a long week of travel, we decided to do something to relax on the train ( to kill time). Since we happened to mention Minecraft and MagicaVoxel, we decided to do a Hackathon, where we use Taichi Lang to create a GPU path tracing voxel renderer. Soon, before we were back home, we had our prototype:
In our recently published blog Is Taichi Lang able to make better use of the underlying hardware than other native, low-level programming languages? With this question in mind, we kick-started the benchmark project in an attempt to provide a comprehensive and accurate performance evaluation of Taichi Lang.
Physical simulation, which Taichi Lang is best at, has wide applications on mobile devices, such as real-time physically-based interactions in mobile games or cool visual effects in short videos. This is thanks to Taichi's features such as fast prototyping and cross-platform GPU acceleration.
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