Taichi Blogs

Numerical simulation and computer graphics usually involve collision detection of a massive number of particles (in many cases, millions of particles). Regular operations, such as particle movement and boundary handling, can be handled in O(N) time complexity (N refers to the number of particles). But the complexity of collision detection can easily escalate to O(N^2) if no optimization is made, imposing an algorithmic bottleneck. A commonly-used technique is grid-based neighborhood search. By confining the search for collision-prone particles to a small area, we can reduce the computational complexity of collision detection back to O(N). This article takes a minimal 2D discrete element method (DEM) solver as an example and presents a highly efficient implementation of neighborhood search using Taichi's data structures.

Nvidia unveiled its Tesla V100 GPU accelerator, which has since become a must-have model for deep learning, at GTC (GPU Technology Conference) 2017 in Beijing. It was on the same occasion that Jensen Huang, Nvidia's CEO, solemnly gave us the most sincere advice, which kept resonating in our heads for years to come:

Starting from v1.1.0, Taichi provides quantized data types. But why is quantization important, especially in scenarios where Taichi stands out, such as physical simulation? This blog demonstrates how this new feature reduces your GPU memory usage significantly and requires zero change to your computational code.

GPU-accelerated image processing tutorial

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.