Regarding the speed of GoL itself: The JAX implementation of GoL he used should be able to do 10 billion cells / second http://www.bnikolic.co.uk/blog/python/

Regarding the speed of GoL itself: The JAX implementation of GoL he used should be able to do 10 billion cells / second http://www.bnikolic.co.uk/blog/python/jax/2020/04/19/game-of-life-jax.html http://www.bnikolic.co.uk/blog/python/jax/2020/04/19/game-of... > Speed of execution > Looking into speed of execution was not a primary goal, but in case people are interested initial: study suggests this code will do about 10 billion cells / second on the Google Colab GPU runtime. For comparision this simple numba optimized GoL propagation function achieves roughly one billion cells per second on CPU (i7-9700K): @numba.jit def propagate_jit(arr, result): h, w = arr.shape for y in range(1,h-1): for x in range(1,w-1): result[y,x] = arr[y,x] num_neighbors = ( arr[y-1,x-1] + arr[y-1,x] + arr[y-1,x+1] + arr[y,x-1] + arr[y,x] + arr[y,x] + arr[y+1,x-1] + arr[y+1,x] + arr[y+1,x+1] ) if num_neighbors == 3: result[y,x] = 1 elif num_neighbors < 2 or num_neighbors > 3: result[y,x] = 0 arr = np.random.randint(0,2,(700,480)) arr2 = arr.copy() %timeit propagate_jit(arr, arr2) # 333 µs ± 2.64 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 700*480*1e6/333 # 1009009009.009009 Btw: The not numba optimized implementation will run in 806ms on my system. The optimization gives a speedup of over 2400.