4p
Similar to the CUDA_brute_triangle project, but this implementation brute-force examines all N choose 4 2-D points and finds which polygon(s) contain the greatest number of points inside.
Not quite as fast as the 3 point version, but much faster than a serial implementation. If you want an estimate of a multi-core implementation you can divide the CPU time by the number of cores (not really that simple in real life). Even then much much faster than a multi-core implementation, and this performance despite the fact there is a great deal of 64-bit integer operations which are much slower than 32-bit operations on consumer GPUs.
CPU used= Intel i7 4820k 4.5 GHZ
GPU used= NVIDIA GTX 1080 1.8 Ghz
CUDA 8.0 RC
Windows 7 x64
compile with --use_fast_math flag for better performance
| NumPoints | NumCombosEvaluated | 4.5 Ghz CPU time | 1.8 Ghz GPU time | CUDA Speedup |
|---|---|---|---|---|
| 100 | 3,921,225 | 4,245 ms | 9 ms | 471.6x |
| 200 | 64,684,950 | 148,411ms | 250 ms | 593.6x |
Still think this can be improved, but in general this technique works and will run even faster on a Pascal Titan X, so will post those results soon.
Can handle up to 2000 points, but any more will overflow constant memory.
Linux users need to change the long long(a) casts to (long long)a . Does need a GPU will compute capability >3.0.
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