SimpleHydrology VS lily-pad

This was generated using an erosion simulation (source-code: https://github.com/weigert/SimpleHydrology).

You can find the code here: https://github.com/weigert/SimpleHydrology

SimpleHydrology now supports infinitely large maps. This was solved by making a memory pool for all map properties, requesitioning buckets for individual map-nodes and rewriting the erosion code to run on these chunks. The limit is really your memory now (and patience).

Full, updated and cleaned code now available here.

The full code is available here. Written in C++ with TinyEngine.

I have been playing with my SimpleHydrology system [Github Link] over the holidays and decided to implement momentum conservation for the particle-based erosion system. This, together with some minor parameter tweaks, lets me get nice meandering river behavior.

This is a simulation of wind flow over procedural terrain using my own GPU accelerated implementation of the lattice boltzmann method. All implemented and visualized in TinyEngine (by me). Terrain generated using SimpleHydrology (also by me). The source for this is not released (yet!). This scene is 100% procedural.

With a better visualization of the streamlines, some performance optimization and nicer procedural terrain (generated using SimpleHydrology) this looks really nice!

Sure, full source here.

Sure, I can outline some basics. You can also read this thread for a small discussion.

As for CPU vs GPU, I would never presume to judge your preferences here. You are absolutely right that it is easier to write for the CPU, and moreover, it is much easier to write code that can't be massively parallel. If time or possibility are your limiting factor, CPU is definitely the way to go. In fact, I was recently looking at this neat project that simulates real time fluids with solid objects obstructing the flow. It is done on the CPU, and I have no idea if it would be possible on the GPU. I even started my own simulation as a parallel CPU algorithm. I did not port it until I had a very solid understanding of what I was doing, but I will say the performance increase was truly astounding. I went from managing 200-300 iterations in 20 seconds on 12 SMT CPU cores, to processing 10,000 iterations with 16 times the data in just a few seconds. So if you have something that can be ported to the GPU, there is a lot to gain from it. This kind of performance boost can make impractical applications practical.