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Taichi Blogs

How Taichi Fuels GPU-accelerated Image Processing: A Beginner to Expert Guide
November 4, 2022 | Yuanming Hu, Liang Zhao
GPU-accelerated image processing tutorial
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Accelerate Python code 100x by import taichi as ti
August 23, 2022 | Yuanming Hu
Python has become the most popular language in many rapidly evolving sectors, such as deep learning and data sciences. Yet its easy readability comes at the cost of performance. Of course, we all complain about program performance from time to time, and Python should certainly not take all the blame. Still, it's fair to say that Python's nature as an interpreted language does not help, especially in computation-intensive scenarios (e.g., when there are multiple nested for loops).
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Taichi & PyTorch 02: Data containers
August 15, 2022 | Ailing Zhang
In my last blog, I compared the purposes and design philosophies of Taichi Lang and PyTorch. Now, it's time to take a closer look at their data containers - the most essential part of any easy-to-use programming language.
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Taichi & PyTorch 01: Underlying difference
August 8, 2022 | Ailing Zhang
"How does Taichi differ from PyTorch? They are both embedded in Python and can run on GPU! And when should I choose Taichi over PyTorch or the other way around?"
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From molecular simulation to black hole rendering - Taichi Lang makes life easier for digital content creators
July 29, 2022 | Yuanming Hu
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.
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How I created the tranquil autumn air within 99 lines of Python code
May 16, 2022 | Yuanming Hu
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:
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Why a New Programming Language
February 18, 2022 | Ye Kuang
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.
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Head First Taichi: A Beginner's Guide to High Performance Computing in Python
October 12, 2021 | Dunfan Lu
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:
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