As I mentioned in last post, all my data are in the GPU side. And according to the existing algorithm, there is no conflict while updating the built-in KD tree. So I decide to update each agent in parallel, but some modification is needed.
For simpleAI, two steps are taken actually in the critical method updateAI in the SimpleAgent class.
First, a agent will check whether it reaches the goal already, if it reaches it, then it will confirm whether the reached goal is the last. If it is, then the agent will disable itself and finishes the update, otherwise, the subsequent goal will be set as the current goal. Afterwards, a simple movement will be calculated, and the agent will update its position and the location in the KD tree. The annoying thing here is that invoking relocating object in KD tree is at CPU side, so it is impossible to make it run at GPU side. Therefore, what I did is storing the newBounds and oldBounds in the agent, then copy them to CPU side and finish the update part.
For pprAI, the idea is similar but taking more steps. perceptive, predictive, and reactive steps could be done in one big step, but beforehand, the required query should be done. Afterwards, the updating part as we did in simpleAI would be the same in pprAI.
Hopefully, I can finish simpleAI this week, and march to modify pprAI next week. I will keep the blog updated.
Perhaps you are too short on time, but have you considered sorting the KD-tree all GPU-side?
ReplyDeleteYes. But I am too short of time, and for simpleAI, there is no inquiry of neighbors.
DeleteIf I have time, I will try to port KD-tree to GPU-side. But it is not guaranteed.
Thanks for your suggestion.