Wednesday, November 16, 2011

It Builds!

This image might not look like much to you, but after an entire weekend dedicated to resolving linker errors, that's pretty huge for me.  I can now include files in my .cu files.  Now the fun begins.

Friday, November 11, 2011

Above is the video I made for my checkpoint presentation this past Monday.  Noticeably absent from it is any mention of algorithms I plan to implement in the AI. 

The update method for the simpleAI will take the position of the agent and the goal and just compute the vector between them.  The agent will then be moved some distance along this vector depending on its speed and acceleration.

To begin the more complicated AI, I will probably start by implementing R* on the GPU.  R* is a suboptimal variant of A* that lends itself better to running in parallel.  After that I will probably try to implement a space-time prediction algorithm like the one described in the paper A Modular Framework for Adaptive Agent-Based Steering (Singh et al.)

My hope is to get simpleAI running on the GPU this weekend, and then I will post some video and metrics of the CPU vs the GPU simulations.

Saturday, November 5, 2011

Finally got steersuite (the framework I'm using) up and running.  Look at all those agents!  My plan now is to try and farm off agents to the GPU.  I may post a video soon, if I get something exciting to show.

Friday, November 4, 2011

It's about time I started posting here about my progress with this project.  First off, what is this project?  The idea is to create an agent based artificial intelligence on the GPU, which will hopefully be able to handle more agents more efficiently that a similar AI on the CPU.  Here are the steps.

Step 0:
- Farm each agent to a thread. This would be simply parallelizing the
for loop which executes update for each agent.
- There will be a result at the end of this which makes for a
good milestone.

Step 1:
- Naive decomposition of agents by taking positions into account (by recursively split the world into quadrants).

Step 2:
- kd-tree decomposition. this is more intelligent spatial decomposition.
- I was given a kd-tree implementation, so I will have to change it to suit my purpose.

Step 3 (independent of Steps 1,2)
- parallelizing the grid database (the database which stores
information of all agent positions)
- parallelizing the drawing loop (this is very similar to step 0 where I simply parallelize a for loop)

 That's it for now, although I will post some pictures later when I get Steersuite (the framework I'm using, which was provided to me by Mubbasir Kapadia) up and running.