mirror of
https://github.com/PacktPublishing/Hands-On-GPU-Programming-with-CUDA-C-and-Python-3.x-Second-Edition.git
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90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
# Iterative version of Conway's game of life in Python / CUDA C
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# written by Brian Tuomanen for "Hands on GPU Programming with Python and CUDA"
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import pycuda.autoinit
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import pycuda.driver as drv
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from pycuda import gpuarray
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from pycuda.compiler import SourceModule
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import numpy as np
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import matplotlib.pyplot as plt
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ker = SourceModule("""
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#define _X ( threadIdx.x + blockIdx.x * blockDim.x )
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#define _Y ( threadIdx.y + blockIdx.y * blockDim.y )
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#define _WIDTH ( blockDim.x * gridDim.x )
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#define _HEIGHT ( blockDim.y * gridDim.y )
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#define _XM(x) ( (x + _WIDTH) % _WIDTH )
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#define _YM(y) ( (y + _HEIGHT) % _HEIGHT )
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#define _INDEX(x,y) ( _XM(x) + _YM(y) * _WIDTH )
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// return the number of living neighbors for a given cell
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__device__ int nbrs(int x, int y, int * in)
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{
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return ( in[ _INDEX(x -1, y+1) ] + in[ _INDEX(x-1, y) ] + in[ _INDEX(x-1, y-1) ] \
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+ in[ _INDEX(x, y+1)] + in[_INDEX(x, y - 1)] \
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+ in[ _INDEX(x+1, y+1) ] + in[ _INDEX(x+1, y) ] + in[ _INDEX(x+1, y-1) ] );
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}
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__global__ void conway_ker(int * lattice, int iters)
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{
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// x, y are the appropriate values for the cell covered by this thread
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int x = _X, y = _Y;
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for (int i = 0; i < iters; i++)
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{
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// count the number of neighbors around the current cell
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int n = nbrs(x, y, lattice);
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int cell_value;
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// if the current cell is alive, then determine if it lives or dies for the next generation.
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if ( lattice[_INDEX(x,y)] == 1)
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switch(n)
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{
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// if the cell is alive: it remains alive only if it has 2 or 3 neighbors.
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case 2:
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case 3: cell_value = 1;
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break;
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default: cell_value = 0;
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}
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else if( lattice[_INDEX(x,y)] == 0 )
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switch(n)
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{
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// a dead cell comes to life only if it has 3 neighbors that are alive.
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case 3: cell_value = 1;
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break;
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default: cell_value = 0;
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}
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__syncthreads();
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lattice[_INDEX(x,y)] = cell_value;
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__syncthreads();
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}
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}
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""")
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conway_ker = ker.get_function("conway_ker")
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if __name__ == '__main__':
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# set lattice size
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N = 32
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lattice = np.int32( np.random.choice([1,0], N*N, p=[0.25, 0.75]).reshape(N, N) )
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lattice_gpu = gpuarray.to_gpu(lattice)
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conway_ker(lattice_gpu, np.int32(100000), grid=(1,1,1), block=(32,32,1))
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fig = plt.figure(1)
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plt.imshow(lattice_gpu.get())
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plt.show()
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