2 posts tagged with "tutorial"

"What is the advantage of Taichi over Pytorch or Tensorflow when running on GPU?" Not surprisingly, this is one of the top questions we've received in the Taichi user community. In this blog series we will walk you through some major concepts and components in Taichi and Torch, elaborating on the analogies and differences which might not be obvious at the first sight.

Let’s start with a simple fact. Except for some minor intersections, Taichi and Torch target almost completely different users and applications. Torch is always your first choice for deep learning tasks like computer vision, natural language processing and so on. Taichi, on the other hand, specializes in parallel high-performance numerical computational tasks and really comes in handy when it comes to physics simulation and visual computing tasks.

From a high-level perspective, Taichi looks very similar to Torch in the sense that their main goals are both to lower the bar for their users. Compared to the static computation graph-based Tensorflow 1.0, Torch eager mode changes the game by building the graph on the fly as your python program runs. Similarly, Taichi aims to enable more people to write high-performance parallel programs that used to require a lot of domain knowledge in CUDA, OpenGL, or Vulkan.

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:

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
...

Simple is better than complex. Readability counts. No doubt, Python has indeed been quite successful at achieving these goals: it is by far the most friendly language to learn, and an average Python program is often 5-10 times shorter than equivalent C++ code. Unfortunately, there is a catch: Python's simplicity comes at the cost of reduced performance. In fact, it is almost never surprising for a Python program to be 10-100 times slower than its C++ counterpart. It thus appears that there is a perpetual trade-off between speed and simplicity, and no programming language shall ever possess both.

But don't you worry, all hope is not lost.