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University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 1 - Flocking

  • Rony Edde
  • Tested on: Personal Laptop Windows 22, i7-6700k @ 4.00GHz 64GB, GTX 980M 8192MB (home)

(TODO: Your README)

Include screenshots, analysis, etc. (Remember, this is public, so don't put anything here that you don't want to share with the world.)

Title changes and performance analysis

Boids simulation running 3 implementation versions.

  • The Naive method consists of a neighbor search through all boids This is slow due to the large lookup.

  • The sparse grid lookup where all boids are indexed using a voxel grid to identify nearby cells. This is much faster because we only search boids that are in neighboring cells.

  • The coherent grid where instead of indexing the particle pointers in order to find their corresponding velocity and positions, we sort the position and velocity attributes directly.

  • Naive method: default5000bruteforce

  • Sparse Grid method: default100000sparsegrid

  • Coherent Grid method: default100000coherentgrid

  • Performance: The naive solution seems fast enough with 5000 points running on a GTX980M but increasing this to 10000 and we see a drop by half the framerate and exponentially slower the higher the number.

    • Here have a few CUDA compute average results on 5000 points:

      • Naive:
        • Average CUDA frame time 3.584352 ms
        • Average CUDA frame time 3.646981 ms
        • Average CUDA frame time 3.643696 ms
      • Saprse Grid:
        • Average CUDA frame time 1.676956 ms
        • Average CUDA frame time 2.982781 ms
        • Average CUDA frame time 3.228212 ms
      • Coherent Grid:
        • Average CUDA frame time 1.936160 ms
        • Average CUDA frame time 1.930368 ms
        • Average CUDA frame time 2.040167 ms

      The performance is a bit faster with the sparse and coherent solutions and it looks like the coherent solution is a litte faster but overall not much difference can be seen with 5000 points.

      • Pushing the limit further reveals more. Here are the results when simulating 100,000 points:

      • Naive:

        • Average CUDA frame time 2190.682983 ms
        • Average CUDA frame time 2174.661621 ms
        • Average CUDA frame time 2174.265381 ms
      • Saprse Grid:

        • Average CUDA frame time 16.067091 ms
        • Average CUDA frame time 22.827206 ms
        • Average CUDA frame time 27.569127 ms
      • Coherent Grid:

        • Average CUDA frame time 18.013756 ms
        • Average CUDA frame time 21.239281 ms
        • Average CUDA frame time 21.476675 ms
      • Here's a graph of performance tests running from 5000 to 50000 incrementing by 5000: testAll testGridOnly

      Here we can clearly see the advantage of using grids. Especially the coherent solution which reduces index lookup.

      • Increasing the number of points clearly impacts performance as seen in the difference between the 5000 point benchmark and this last benchmark. This is obviously due to the increased number of points to search for. The naive method takes a bigger hit since the search involves all points whereas the sparse and coherent grid solutions are less impacted but still impacted due the increased concentration of points in cells. Here are the results for 100,000 points

      • Now there are cases where the sparse and coherent grid fail. This is when the search radius becomes so small that the number of cells exceeds the graphics card memory and will crash. The solution to this would be to limit the size in order to prevent such a scenario. This is commented out in the final code release. So provided we clamp the minimum cell size, it should be safe to use the sparse and coherent grid.

Here are a few performance analysis with NSIGHT: * Bruteforce: default100000bruteforceperformance

* Sparce Grid:

default100000sparsegridperformance

* Coherent Grid:

default100000coherentgridperformance