Taichi Blogs

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.

Hey guys! Welcome to my first Taichi cooking session!

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.

In the previous blog post, we mentioned this sentence, which is a part of the zen of Python. In this post, we will show you how we simplified the code of Taichi.

Imagine you'd like to write a new particle-based fluid algorithm. You started simple, didn't spend much time before finding a reference C++/CUDA work online (or derived the work from your labmate, unfortunately). cmake .. && make, you typed. Oops, cmake threw out an error due to a random incompatible third party library. Installed and rebuilt, now it passed. Then you ran it, which immediately segfaulted (without any stacktrace, of course). Then you started gazing at the code, placed the necessary asset files at the right place, fixed a few dangling pointers and reran. It... actually worked, until you plugged in your revised algorithm. Now another big fight with the GPU or CPU code. More often than not, you get lost in the language details.

Ever since the Python programming language was born, its core philosophy has always been to maximize the readability and simplicity of code. In fact, the reach for readability and simplicity is so deep within Python's root, that if you type import this in a Python console, it will recite a little poem: