faster-python-with-taichi VS taichi

You can refer to the source code of three examples comparing Taichi with Python here.

A shortcut to the source code of the three cases: https://github.com/taichi-dev/faster-python-with-taichi.

Do you need to use a graphics API like Vulkan, or does another compute API like OpenCL or AMD's HIP fit the bill better? Maybe even something like Taichi? If you just want to run things in parallel on the GPU there are other options that hold your hand a bit more.

I would love to see the OP benchmarks with Taichi applied: https://github.com/taichi-dev/taichi

You should check out taichi: https://github.com/taichi-dev/taichi They have a ton of great demos for doing physics but check out this example in particular for something related to your project: https://github.com/taichi-dev/quantaichi#game-of-life-gol. (Taichi also makes it super easy to write things for the GPU and the kernels are differentiable :).)

Source code of Taichi: https://github.com/taichi-dev/taichi

If you are interested, join the Taichi community: https://github.com/taichi-dev/taichi. Any contribution & feedback is welcome!

I started the project while I was a Ph.D. student at MIT, and later a few dozen people joined the GitHub community (https://github.com/taichi-dev/taichi). After graduation from MIT, I co-founded a company, which has a small team serving as the backbone of this open-source community.

It is not hard to tell that the two groups have their needs overlapped to a large extent. To improve user experience, we will continue to optimize our CHI (Chi Hierarchical Instructions) IR, enhance modularization and reusability, and keep abreast of CUDA, Vulkan, and Metal developments. Specifically, we hope to further reduce the compile time for Group A users, and CHI JIT Cache is one such attempt. Meanwhile, we are proceeding with the AOT development, including the Foreign Function Interface (FFI) feature, so that Group B users can use Taichi more comfortably.