taichi_benchmark VS awesome-taichi

In fact, Taichi's compiler relies on heavy underlying engineering to enable high performance. In the aforementioned code snippet, the summation is actually an atomic operation, which cannot be parallelized and is thus subject to limited operation efficiency. The most commonly used method of parallel computing optimization in vector summation is reduction, which is one of the must-have skills to learn parallel computing. The benchmark report concludes that, thanks to automatic reduction optimization implemented by its compiler, Taichi achieves a performance comparable to manually implemented CUB and way better than Numba.

Sure, here are some benchmarks that could be helpful: https://github.com/taichi-dev/taichi_benchmark

Some are uncertain about Taichi Lang's performance against other frameworks such as CUDA, and would require a comprehensive benchmarking report. Actually, we published a systematic benchmarking report when we released Taichi Lang v1.0.0. Taichi has more or less comparable performance to CUDA. But, for sure, you will have much fewer lines of code with Taichi Lang!

Awesome Taichi repo: https://github.com/taichi-dev/awesome-taichi

To have a better understanding of the scenarios where Taichi is (spontaneously) applied, we launched taichi-dev/awesome-taichi to collect and present top-notch Taichi-empowered projects. Most of the examples given below are available in this repo.

The Taichi community is active and provides a wide range of reference codes.