Taichi Blogs

Imagine this: when you flip through a photo album and see pictures of past family trips, do you want to revisit those places and relive those warm moments? When browsing an online museum, do you want to freely adjust your perspective, observe the details of the exhibits up close, and enjoy a full interaction with the cultural relics? When doctors face patients, can they significantly improve diagnostic accuracy and efficiency by synthesizing a 3D perspective of the affected area based on images and providing estimates of lesion size and volume?

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

In the previous blog, we learned that Taichi, a high-performance computing language embedded in Python, goes beyond a development tool for computer graphics and renderers but also comes in handy for numerical computation that involves massive operations on 2D and 3D arrays. Computational fluid dynamics (CFD) is a typical scenario where Taichi can play a part.

Computational fluid dynamics (CFD) is a branch of fluid mechanics that endeavors to precisely reproduce the behavior of liquid/gas flows and their interaction with solid boundaries. It plays a vital role in such sectors as visual effects, virtual reality, and industrial design.

Our previous blogs (Taichi & PyTorch 01 and 02) pointed out that Taichi and Torch serve different application scenarios can they complement each other? And the answer is an unequivocal yes! In this blog, we will use two simple examples to explain how to use Taichi kernel to implement data preprocessing operators or custom ML operators. With Taichi, you can accelerate your ML model development with ease and get rid of the tedious low-level parallel programming (CUDA for example) for good.

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).

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.